Motorized system integrated control and diagnostics using vibration, pressure, temperature, speed, and/or current analysis

ABSTRACT

Systems and methods are disclosed for controlling, diagnosing and prognosing the health of a motorized system. The systems may comprise a diagnostics system, a prognostic system and a controller, wherein the diagnostics system and/or prognostic system employs a neural network, an expert system, and/or a data fusion component in order to assess and/or prognose the health of the motorized system according to one or more attributes associated therewith. The controller may operate the motorized system in accordance with a setpoint and/or a diagnostics signal from the diagnostics system and/or prognostic information. Also disclosed are methodologies for controlling, diagnosing and prognosing the health of a motorized system, comprising operating a motor in the motorized system in a controlled fashion, diagnosing and/or prognosing the health of the motorized system according to a measured attribute associated with the motorized system, wherein the motor may be operated according to a setpoint and/or the diagnostics signal and/or prognosis.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation-in-part of U.S. patent applicationSer. No. 09/866,414, filed May 25, 2001, and entitled MOTORIZED SYSTEMINTEGRATED CONTROL AND DIAGNOSTICS USING VIBRATION, PRESSURE,TEMPERATURE, SPEED AND/OR CURRENT ANALYSIS, which is acontinuation-In-part of the following: U.S. patent application Ser. No.09/163,933, filed Sep. 29, 1998, and entitled MACH/NE DIAGNOSTIC SYSTEMAND METHOD FOR VIBRATION ANALYSIS, now issued as U.S. Pat. No.6,289,735; U.S. patent application Ser. No. 09/407,617, filed Sep. 28,1999, and entitled DETECTION OF PUMP CAVITATION/BLOCKAGE AND SEALFAILURE VIA CURRENT SIGNATURE ANALYSIS, now issued as U.S. Pat. No.6,757,665; and U.S. patent application Ser. No. 09/461,787, filed Dec.15, 1999, and entitled INTEGRATED CONTROL AND DIAGNOSTICS SYSTEM, nowissued as U.S. Pat. No. 6,326,758, the disclosures of which are herebyincorporated by reference as if fully set forth herein.

FIELD OF THE INVENTION

The invention described below generally relates to controlling anddiagnosing the health of a machine, and more particularly, to systemsand methods for controlling and diagnosing motorized systems accordingto vibration, pressure, temperature, speed, and/or current analysis.

BACKGROUND OF THE INVENTION

Many industrial processes and machines are controlled and/or powered byelectric motors. Motorized systems include pumps providing fluidtransport for chemical and other processes, fans, conveyor systems,compressors, gear boxes, motion control devices, screw pumps, andmixers, as well as hydraulic and pneumatic machines driven by motors.Such motors are combined with other system components, such as valves,pumps, conveyor rollers, fans, compressors, gearboxes, and the like, aswell as with appropriate motor drives, to form industrial machines andactuators. For example, an electric motor may be combined with a motordrive providing electrical power to the motor, as well as with a pump,whereby the motor rotates the pump shaft to create a controllablepumping system.

Controls within such motorized systems provide for automatic systemoperation in accordance with a setpoint value, and may additionallyallow for manual operation. Thus, for instance, a motorized pump systemmay be operated so as to achieve a user specified outlet fluid flowrate, pressure, or other system setpoint. In another example, amotorized conveyor system may include one or more motorized rollersystems, wherein the individual roller systems are controlled accordingto a conveyor speed setpoint. Such motorized system controls may includea controller receiving a setpoint from a user or from another system,which inputs one or more system performance values and providesappropriate control signals to cause the motorized system to operate ina controlled fashion according to a control scheme. For example, amotorized pump system may be controlled about a flow rate setpoint,wherein the associated controller reads the setpoint from a userinterface, measures the system outlet flow rate via a flow sensor, andprovides a control signal indicative of pump speed to a motor driveoperatively connected to a motorized pump, whereby the control signal isadjusted so as to achieve the setpoint flow rate in closed-loop fashion.

Although operation of such motorized systems and controllers may achievesystem operation in accordance with the setpoint, other factors such assystem component wear, component faults, or other adverse conditions,and the like, may affect the operation of the motorized system. Thus,for example, degradation in a pump impeller in a motorized pumpingsystem may lead to premature catastrophic failure of the system if leftunchecked. In this regard, operation of the pump strictly in accordancewith a flow rate setpoint may accelerate the system component wear,degradation, and/or failure, whereas operation at other flow rates mayallow the system to last longer. This may be of importance in criticalsystems where safety is an issue. For instance, the motorized pumpingsystem may be located on board a military vessel at sea, whereinoperation according to a flow setpoint may lead to catastrophic pumpimpeller or seal failure before the vessel can be brought to port forrepairs or maintenance, whereas system operation at a reduced flow ratemay allow the pump to survive until the next scheduled servicing.

Motor diagnostics apparatus has been employed in the past to provide anindication of wear, damage, and/or degradation in motor systemcomponents, such as rotors, bearings, or stators, prior to catastrophicfailure thereof. Such diagnostic devices may be used to monitor theoverall health of either the motorized system components beingcontrolled, or the control system itself. In this regard, assessingsystem health can be used to minimize unscheduled system downtime and toprevent equipment failure. This capability can avoid a potentiallydangerous situation caused by the unexpected outage or catastrophicfailure of machinery. However, many conventional diagnostic devicesinconveniently require an operator to manually collect data frommachinery using portable, hand-held data acquisition probes.

Other known systems have sensors and data acquisition and networkequipment permanently attached to critical machinery for remotediagnostics. Typically the diagnostics equipment is directed todetecting problems with the process control system hardware itself ormonitoring the integrity of the output, i.e., monitoring when theprocess response is outside prescribed time or value limits. As notedabove, system health monitoring, health assessment and prognosticsgenerally are performed in isolation from any associated control system.These systems typically conduct passive monitoring and assess systemhealth using diagnostic algorithms and sensors dedicated to establishsystem health. This passive monitoring is frequently done usingoff-line, batch-mode data acquisition and analysis to establish thehealth of the system.

In conventional motorized systems, therefore, controlled operation isprovided about a setpoint, wherein such controlled setpoint operationmay exacerbate system component degradation and/or acceleratecatastrophic failure thereof. Prior diagnostics apparatus achieves somelevel of identification of such system component degradation prior tocomponent failure. However, as noted previously, because virtually alldiagnostics systems perform off-line diagnostic processing, it has beenextremely difficult to implement diagnostics processing real-time incoordination with on-line control. Thus there is a need for improvedcontrol and diagnostics systems and techniques by which controlledoperation of motorized systems can be achieved while mitigating theextent of component degradation and failure.

SUMMARY OF THE INVENTION

The following presents a simplified summary of the invention in order toprovide a basic understanding of one or more aspects of the invention.This summary is not an extensive overview of the invention. It isintended to neither identify key or critical elements of the invention,nor to delineate the scope of the present invention. Rather, the solepurpose of this summary is to present some concepts of the invention ina simplified form as a prelude to the more detailed description that ispresented hereinafter. The invention provides control systems andmethodologies for controlling and diagnosing the health of a motorizedsystem and/or components thereof. Diagnosis of the system or componenthealth is accomplished using advanced analytical techniques such asneural networks, expert systems, data fusion, spectral analysis, and thelike, wherein one or more faults or adverse conditions associated withthe system may be detected, diagnosed, and/or predicted.

The diagnostics may be performed using one or more measured systemparameters, such as vibration, pressure, temperature, speed, power,current, or the like. Frequency spectral analysis may also be employedin order to detect and/or diagnose component wear, degradation, failure,faults, and the like. For example, one or more signals from systemsensors may be processed by a diagnostics system and analyzed in thefrequency domain, whereby such adverse conditions may be identifiedand/or predicted. The diagnostics system may further provide one or morediagnostics signals indicative of the health of a motorized system,which may then be provided to an associated controller, whereby theoperation of the motorized system may be modified. In this regard, thesystem operation may be modified to ameliorate detected component orsystem degradation. Subsequent diagnostics on the system with modifiedcontrol can confirm, in a feedback operation, whether or not the rate ofcomponent degradation has been favorably affected, and/or whether a new,extended operating lifetime will be obtained.

The invention may thereby provide for extending the useful service lifeof one or more system components. Thus, the invention finds utility inassociation with a variety of industrial processes and machines whichare controlled and/or powered by electric and other types of motors.Such processes and machines include pumps providing fluid transport forchemical and other processes, fans, conveyor systems, compressors, gearboxes, motion control devices, screw pumps, and mixers, as well ashydraulic and pneumatic machines driven by motors.

According to an aspect of the present invention, there is provided adiagnostics and control system for controlling a motorized system anddiagnosing and/or predicting the health thereof. The diagnostics andcontrol system may comprise a controller operatively associated with themotorized system and adapted to operate the motorized system in acontrolled fashion, and a diagnostics system operatively associated withthe motorized system and adapted to diagnose the health of the motorizedsystem according to a measured attribute associated with the motorizedsystem. The measured attribute may comprise vibration, pressure,current, speed, and/or temperature, for example, wherein such attributeinformation is obtained from one or more sensors operatively connectedto the motorized system.

The diagnostics system may provide a diagnostics signal according to thehealth of the motorized system, and the controller may accordinglyprovide a control signal to the motorized system according to a setpointand/or the diagnostics signal. For example, the controller may compriseone or more changeable parameters, such as gains or the like, whereinthe parameter may be changeable in response to the diagnostics signal.In this manner, the operation of the motorized system may be adaptableto a variety of system health conditions, whereby the adverse effects ofsystem health problems may be proactively addressed in order to mitigatethe effects thereof, including for example, the extension of servicelife and the reduction in system downtime and/or failures. Moreover, theoutput of the controller may be provided to the diagnostics system sothat the health assessment made by the diagnostics system can be basedat least in part on the control signal and the response by the motorizedsystem to this control action. Alternatively or in combination, processconditions observed may be compared to models of the motor, pump, andhydraulic system components (e.g., pipes, valves, etc.) in order toestablish a diagnosis of the system.

In addition to component diagnostic information, the invention providesfor detecting, diagnosing, and/or adaptive control according to otherdetected system operating conditions. For instance, the diagnosticssystem may be advantageously employed in order to detect and diagnosecavitation, blockage, and/or the like in a motorized pump or in thesystem in which the motorized pump is employed. In this regard, pressureand flow information related to the pumping system may be measured andcavitation conditions may be identified using a classifier system, suchas a neural network. The diagnostics system may thus comprise such aclassifier system for detecting pump cavitation according to flow andpressure data. The invention may be employed in cavitation monitoring,as well as in control equipment associated with pumping systems, wherebypump wear and failure associated with cavitation conditions may bereduced or eliminated.

Such pumping system cavitation conditions may alternatively or incombination be diagnosed using other measured signals from the system.For instance, it has been found that fault data relating to theoperating condition of a motorized pump may be ascertained fromvariations in current of a motor driving the pump. These featurespresent in the stator frequency spectrum of the motor stator current maybe caused by load effects of the pump on the motor rather than changesin the motor itself. The present invention provides a system and methodfor extracting (e.g., synthesizing) the fault data directly from theinstantaneous motor current data. This data relates not only to pumpmachinery conditions, but also pump process conditions. Thus, byemploying current signature analysis of the instantaneous current of themotor driving the pump, problems with the pump and/or process line canbe detected without using invasive and expensive pressure and flowmeters. Instead, a lower cost current sensor may be used and this sensormay be located in a motor control center or other suitable locationremote from the motor and pump.

Artificial neural networks (ANN) may thus be employed analyze thecurrent signature data of the motor that relates to pump faults.Although, multi-iterative, supervised learning algorithms could be used,which could be trained and used only when a fully-labeled data setcorresponding to all possible operating conditions, the application ofunsupervised ANN techniques that can learn on-line (even in a singleiteration) may be provided in accordance with the present invention. Thecurrent signature analysis, moreover, may be performed both on the pumpto determine the operating state of the pump, and on the motor drivingthe pump so as to determine the operating state of the motor,simultaneously.

The present invention also provides for preprocessing of the faultsignature data before it is being used to train an ANN or design adecision module based on ANN paradigms. The preprocessing eliminatesoutliers and performs scaling and bifurcation of the data into trainingand testing sets. Furthermore, it is desired to further post process theoutput generated by unsupervised ANN based decision modules forcondition monitoring applications. This is because unsupervised ANNbased decision modules when presented with a new operating condition canonly signal the formation of a new output entry indicating that apossible new condition has occurred, but is not necessarily able toprovide particular fault information. Post processing is carried out byutilizing the domain knowledge of a human expert to develop an expertsystem, or by correctly classifying this new operating state andencoding this information in a fuzzy expert system for future reference,or by a model of the system and its components, such as an analyticalmodel or a qualitative model.

Another aspect of the invention provides systems and methodologies fordetecting motor faults by space vector angular fluctuation, whichrequire no human intervention or downtime, in order to identify motorfaults such as stator faults, rotor faults, and even imbalances in powerapplied to the motor in a timely fashion. Systems and methodologies areprovided for detecting faults and adverse conditions associated withelectric motors. The methodology provides for analyzing the angularfluctuation in a current space vector in order to detect one or morefaults associated with the motor. Systems are disclosed having adiagnostics component adapted to obtain a space vector from a currentsignal relating to operation of the motor, and to analyze the spacevector angular fluctuation in order to detect motor faults.

Yet another aspect of the invention relates to a system to facilitatecontrolling a motorized system. The system can include one or moresensors for sensing various attributes associated with the motorizedsystem. A diagnostics system diagnoses a state of the motorized systembased at least in part on at least one of the sensed attributes. Aprognostic system makes a prognosis of the motorized system based atleast in part on the at least one sensed attribute and/or the diagnosedstate. A controller controls the motorized system based at least in parton the diagnosed state and/or prognosed state. Thus, the system providesfor not only controlling the motorized system based upon current stateinformation but also upon predicted future state(s).

To the accomplishment of the foregoing and related ends, the invention,then, comprises the features hereinafter fully described. The followingdescription and the annexed drawings set forth in detail certainillustrative aspects of the invention. However, these aspects areindicative of but a few of the various ways in which the principles ofthe invention may be employed. Other aspects, advantages and novelfeatures of the invention will become apparent from the followingdetailed description of the invention when considered in conjunctionwith the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic diagram illustrating an exemplary method inaccordance with an aspect of the present invention;

FIG. 2 a is a side elevation view illustrating a motorized system havingan exemplary diagnostics and control system in accordance with anotheraspect of the invention;

FIG. 2 b is a schematic illustration of a system in accordance with thepresent invention that employs diagnostic and/or prognostic informationin connection with controlling a motorized system;

FIG. 3 is a schematic diagram illustrating an exemplary diagnosticssystem with a neural network in accordance with another aspect of theinvention;

FIG. 4 is a schematic diagram illustrating another exemplary diagnosticssystem with an expert system in accordance with another aspect of theinvention;

FIG. 5 is a schematic diagram illustrating another exemplary diagnosticssystem with a data fusion component in accordance with another aspect ofthe invention;

FIG. 6 is a schematic diagram illustrating another exemplary diagnosticssystem with a data fusion component, and neural network, and an expertsystem in accordance with another aspect of the invention;

FIG. 7 is schematic flow diagram illustrating a portion of an exemplarydiagnostics system in accordance with the invention;

FIG. 8 is a schematic diagram further illustrating the exemplarydiagnostics system of FIG. 7;

FIG. 9 is a schematic diagram illustrating an exemplary cavitationclassification in accordance with the invention;

FIG. 10 is a perspective schematic diagram illustrating an exemplaryneural network in accordance with another aspect of the invention;

FIG. 11 is a function block diagram illustrating the diagnostics andcontrol system in accordance with the present invention;

FIG. 12 is a function block diagram further illustrating the diagnosticsand control system of the present invention;

FIG. 13 is a flow diagram for performing a Fast Fourier Transformationon a conditioned signal received from a motor in accordance with thepresent invention;

FIG. 14 a is a graph of a comparison of a Fast Fourier Transform signalrepresentative of a normal condition of a centrifugal pump and acavitation condition of the same pump in accordance with the presentinvention;

FIG. 14 b is a magnified window of the graph of FIG. 14 a in accordancewith the present invention;

FIG. 14 c is a graph of a comparison of a Fast Fourier Transform signalrepresentative of a normal condition of a centrifugal pump and a leakagecondition of the same pump in accordance with the present invention;

FIG. 14 d is a magnified window of the graph of FIG. 14 c in accordancewith the present invention;

FIG. 14 e is a graph of a comparison of a Fast Fourier Transform signalrepresentative of normal condition of a centrifugal pump and a faultyimpeller condition of the same pump in accordance with the presentinvention;

FIG. 14 f is a magnified window of the graph of FIG. 14 e in accordancewith the present invention;

FIG. 14 g is a table diagram of current signal amplitudes over a rangeof frequencies, which may be employed to facilitate diagnosing anoperating state of a machine in accordance with the present invention;

FIG. 15 is a flow diagram for creating a fault signature from thecurrent spectrum on the conditioned signal received from a motor inaccordance with the present invention;

FIG. 16 is a block diagram of an artificial neural network in accordancewith the present invention;

FIG. 17 is a block diagram of an artificial neural network in accordancewith the present invention utilizing an Adaptive Resonance Theoryparadigm;

FIG. 18 is a block diagram of an artificial neural network in accordancewith the present invention utilizing the second version of the AdaptiveResonance Theory paradigm;

FIG. 19 is a functional schematic diagram of the diagnostics and controlsystem including a stand-alone decision module adapted for pumpdiagnosis in accordance with the present invention;

FIG. 20 is a flow diagram for performing an adaptive preprocessing actfor the stand-alone decision module of FIG. 19 in accordance with thepresent invention;

FIG. 21 is a schematic representation of the Fuzzy Rule Base ExpertSystem in accordance with the present invention;

FIG. 22 is a schematic representation of the stand-alone decision modulein accordance with the present invention;

FIG. 23 is an example of a set of fuzzy rules in accordance with thepresent invention;

FIG. 24 is a schematic representation of a system for diagnosing andcontrolling a plurality of pumps in accordance with the presentinvention;

FIG. 25 is a function block diagram illustrating the diagnostics andcontrol system utilizing wavelets in accordance with the presentinvention;

FIG. 26 is an exemplary plot illustrating sampled three phase currentswith unbalanced harmonics in accordance with the invention;

FIG. 27 is an exemplary plot illustrating sampled current space vectorsfor a balanced and an unbalanced system in accordance with theinvention;

FIG. 28 is an exemplary plot illustrating space vector angularfluctuations in the time domain in accordance with the invention;

FIG. 29 is an exemplary plot illustrating frequency spectrum of spacevector angular fluctuation with fault indicative frequencies inaccordance with the invention;

FIG. 30 is an exemplary plot illustrating an exemplary 2fs real timecomponent obtained via a Goertzel algorithm in accordance with theinvention;

FIG. 31 is an exemplary plot illustrating a fluctuation in amplitude ofa 2fs component due to stator fault in accordance with the invention;

FIG. 32 is an exemplary plot illustrating another exemplary 2fs realtime component obtained via a Goertzel algorithm in accordance with theinvention;

FIG. 33 is an exemplary plot illustrating an amplitude fluctuation in2fs and 4fs components due to rotor resistance imbalance according tothe invention;

FIG. 34 is an exemplary plot illustrating an amplitude fluctuation in2fs and 4fs components due to a broken rotor bar in accordance with theinvention;

FIG. 35 is an exemplary plot illustrating sidebands due to rotorresistance imbalance in accordance with the invention;

FIG. 36 is an exemplary plot illustrating oscillation of 2fs and 2sfscomponents due to rotor asymmetry in accordance with the invention;

FIG. 37 is an exemplary plot illustrating a current space vectorspectrum in accordance with the invention;

FIG. 38 is an exemplary plot illustrating a voltage space vectorspectrum in accordance with the invention;

FIG. 39 is an exemplary plot illustrating a space vector angularfluctuation spectrum with components due to rotor imbalance inaccordance with the invention;

FIG. 40 is an exemplary plot illustrating an amplitude fluctuation in(1−s)2fs and (1+s)2fs components due to a broken rotor bar in accordancewith the invention;

FIG. 41 is an exemplary plot illustrating oscillation of a 2fs componentdue to a broken rotor bar in accordance with the invention;

FIG. 42 is an exemplary plot illustrating a current space vectorspectrum in the presence of a broken rotor bar in accordance with theinvention;

FIG. 43 is an exemplary plot illustrating a voltage space vectorspectrum in the presence of a broken rotor bar in accordance with theinvention; and

FIG. 44 is an exemplary plot illustrating a space vector angularfluctuation spectrum in the presence of a broken rotor bar in accordancewith the invention.

DETAILED DESCRIPTION OF THE INVENTION

The various aspects of the present invention will now be described withreference to the drawings, wherein like reference numerals are used torefer to like elements throughout. The invention provides a diagnosticsand control system for controlling a motorized system and diagnosing thehealth thereof, with a controller operatively associated with themotorized system and adapted to operate the motorized system in acontrolled fashion, and a diagnostics system operatively associated withthe motorized system and adapted to diagnose the health of the motorizedsystem according to a measured attribute associated with the motorizedsystem.

Referring initially to FIG. 1, an exemplary method 2 is illustrated forcontrolling a motorized system and diagnosing the health thereof. Themotorized system can comprise components (e.g. bearings), devices (e.g.motor, pump, fan), subsystems (e.g. motor-drive-pump), and processes(e.g. pump-pipes-fluid-control). The method 2 comprises operating amotor in the motorized system in a controlled fashion, and diagnosingthe health of the motorized system according to a measured attributeassociated with the motorized system. While the exemplary method 2 isillustrated and described herein as a series of blocks representative ofvarious events and/or acts, the present invention is not limited by theillustrated ordering of such blocks. For instance, some acts or eventsmay occur in different orders and/or concurrently with other acts orevents, apart from the ordering illustrated herein, in accordance withthe invention. In addition, not all illustrated blocks, events, or acts,may be required to implement a methodology in accordance with thepresent invention. Moreover, it will be appreciated that the exemplarymethod 2 and other methods according to the invention, may beimplemented in association with the motorized systems illustrated anddescribed herein, as well as in association with other systems andapparatus not illustrated or described.

Beginning at 4, the exemplary method 2 comprises measuring an attributeat 5, wherein the attribute is associated with a motorized system (e.g.,motorized pump, fan, conveyor system, compressor, gear box, motioncontrol device, screw pump, and mixer, hydraulic or pneumatic machine,or the like). The attribute measured at 5 may comprise, for example,vibration, pressure, current, speed, and/or temperature associated withthe motorized system. At 6, the health of the motorized system isdiagnosed according to the measured attribute. A diagnostics signal isprovided at 7, which may be indicative of the diagnosed motorized systemhealth, whereby the motorized system is operated at 9 according to asetpoint and/or the diagnostics signal generated at 7. The provision ofthe diagnostics signal at 7 may comprise obtaining a frequency spectrumof the measured attribute and analyzing the frequency spectrum in orderto detect faults, component wear or degradation, or other adversecondition in the motorized system, whether actual or anticipated. Thediagnosis may further comprise analyzing the amplitude of a firstspectral component of the frequency spectrum at a first frequency. Acontrol recommendation or control signal is generated at 8 based on thediagnosed health of the motorized system.

In order to provide the diagnostics signal, moreover, the invention mayprovide the measured attribute(s) to a neural network, an expert system,a fuzzy logic system, and/or a data fusion component, or a combinationof these, which generates the diagnostics signal indicative of thehealth of the motorized system. For example, such frequency spectralanalysis may be employed at 6 in order to determine faults or adverseconditions associated with the system or components therein (e.g., motorfaults, unbalanced power source conditions, etc.). In addition, thediagnosis at 6 may identify adverse process conditions, such ascavitation in a motorized pumping system. The diagnostics signalgenerated at 7 may be employed by a controller associated with thesystem, whereby modified operation thereof may be performed in order toameliorate or avoid actual or anticipated diagnosed health problems.

Another aspect of the invention provides systems and apparatus forcontrolling and diagnosing the health of motorized systems. Variousaspects of the invention will be hereinafter illustrated with respect toan exemplary motorized pumping system. However, it will be appreciatedby those skilled in the art that the invention finds application inassociation with motorized systems in addition to those illustrated anddescribed herein, including but not limited to motorized pumps, fans,conveyor systems, compressors, gear boxes, motion control devices, screwpumps, mixers, hydraulic or pneumatic machines, or the like.

Referring now to FIGS. 2 a-6, a motorized pump system 12 is illustratedhaving an exemplary diagnostics and control system 66 for controllingthe system 12 and diagnosing the health thereof, in accordance with thepresent invention. As illustrated and described in greater detailhereinafter, the diagnostics and control system 66 comprises acontroller 71 operatively associated with the motorized system 12 andadapted to operate the system 12 in a controlled fashion, and adiagnostics system 70 operatively associated with the motorized system12 and adapted to diagnose the system health according to a measuredattribute. The diagnostics and control system 66 of the invention maymeasure attributes such as vibration, pressure, current, speed, and/ortemperature in order to operate and diagnose the health of the system12. The diagnostics system 70 is operative to diagnose and control anyor all of the controlled system 12, the controller 71, the motor drive60, and incoming power from the power source 62.

An exemplary motorized pumping system 12 is illustrated in FIG. 2 ahaving a pump 14, a three-phase electric motor 16, and a control system18 for operating the system 12 in accordance with a setpoint 19.Although the exemplary motor 16 is illustrated and described herein as apolyphase synchronous electric motor, the various aspects of the presentinvention may be employed in association with single-phase motors aswell as with DC and other types of motors. In addition, while theexemplary pump 14 may comprise a centrifugal type pump, the inventionfinds application in association with other pump types not illustratedherein, for example, positive displacement pumps. The control system 18operates the pump 14 via the motor 16 according to the setpoint 19 andone or more measured process variables, in order to maintain operationof the system 12 commensurate with the setpoint 19 and within theallowable process operating ranges specified in setup information 68.For example, it may be desired to provide a constant fluid flow, whereinthe value of the setpoint 19 is a desired flow rate in gallons perminute (GPM) or other engineering units.

The pump 14 comprises an inlet opening 20 through which fluid isprovided to the pump 14 in the direction of arrow 22 as well as asuction pressure sensor 24, which senses the inlet or suction pressureat the inlet 20 and provides a corresponding suction pressure signal tothe control system 18. Fluid is provided from the inlet 20 to animpeller housing 26 including an impeller (not shown), which rotatestogether with a rotary pump shaft coupled to the motor 16 via a coupling28. The impeller housing 26 and the motor 16 are mounted in a fixedrelationship with respect to one another via a pump mount 30, and motormounts 32. The impeller with appropriate fin geometry rotates within thehousing 26 so as to create a pressure differential between the inlet 20and an outlet 34 of the pump. This causes fluid from the inlet 20 toflow out of the pump 14 via the outlet or discharge tube 34 in thedirection of arrow 36. The flow rate of fluid through the outlet 34 ismeasured by a flow sensor 38, which provides a flow rate signal to thecontrol system 18.

In addition, the discharge or outlet pressure is measured by a pressuresensor 40, which is operatively associated with the outlet 34 andprovides a discharge pressure signal to the control system 18. It willbe noted at this point that although one or more sensors (e.g., suctionpressure sensor 24, discharge pressure sensor 40, outlet flow sensor 38,and others) are illustrated in the exemplary system 12 as beingassociated with and/or proximate to the pump 14, that such sensors maybe located remote from the pump 14, and may be associated with othercomponents in a process or system (not shown) in which the pump system12 is employed. Alternatively, flow may be approximated rather thanmeasured by utilizing pressure differential information, pump speed,fluid properties, and pump geometry information or a pump model.

Alternatively or in combination, inlet and/or discharge pressure valuesmay be estimated according to other sensor signals and pump/processinformation. The system 12 further includes an atmospheric pressuresensor 35, a pump temperature sensor 33 (e.g., thermocouple, RTD, etc.),and a vibration sensor 37 (e.g., accelerometer or the like), providingatmospheric pressure, pump temperature, and pump vibration signals,respectively, to the control system 18. In addition, the system 12 maycomprise a torque sensor (not shown), for example, located at thecoupling 28. The invention finds application in association withmotorized systems having fewer, more, or different combinations ofsensors, apart from the sensors illustrated and described herein,wherein sensors providing signals indicative of other system variablesmay be employed in order to diagnose and control a motorized system inaccordance with the present invention.

In addition, it will be appreciated that while the motor drive 60 isillustrated in the control system 18 as separate from the motor 16 andfrom an exemplary diagnostics and control system 66, that some or all ofthese components may be integrated. Thus, for example, an integrated,intelligent motor may include the motor 16, the motor drive 60 and thediagnostics and control system 66. Furthermore, the motor 16 and thepump 14 may be integrated into a single unit (e.g., having a commonshaft wherein no coupling 28 is required), with or without integralcontrol system (e.g., control system 18, comprising the motor drive 60and the diagnostics and control system 66) in accordance with theinvention. Further, it is appreciated that the motor drive 60 may be asoft-start device such as an SMC. Such a soft start device can turn offthe system for protection and ramp parameters and/or gains may beadjusted to protect equipment (e.g. avoid fluid hammer).

The control system 18 further receives process variable measurementsignals relating to motor (pump) rotational speed, motor temperature,and motor vibration via a speed sensor 46, a motor temperature sensor47, and a motor vibration sensor 48, respectively. As illustrated anddescribed further hereinafter, a diagnostics system 70 within thediagnostics and control system 66 may advantageously detect and/ordiagnose actual or anticipated wear, degradation, failure, and/or faultsassociated with components of the motorized system 12 (e.g., motorbearings, rotor, stator, mounting, alignment, pump bearings, impeller,seals, and/or of components in a larger system of which the motorizedsystem 12 is a part) as well as other system performance conditions,such as cavitation, blockage, or the like.

For instance, the diagnostics system 70 may advantageously be employedin order to detect and/or diagnose cavitation in the pump 14 using aneural network classifier receiving suction and discharge pressuresignals from sensors 24 and 40, respectively, as well as flow and pumpspeed signals from the flow and speed sensors 38 and 46. Alternativelyor in combination, a pump model and/or pump efficiency curves (notshown) may be employed to diagnose faults or other adverse conditions,such as cavitation. The motor 16 provides rotation of the impeller ofthe pump 14 according to three-phase alternating current (AC) electricalpower provided from the control system via power cables 50 and ajunction box 52 on the housing of the motor 16. The power to the pump 14may be determined by measuring the current provided to the motor 16 viaa current sensor 49 and computing pump power based on current, voltage,speed, and motor model information. This may alternatively or incombination be measured and computed by a power sensor (not shown),which provides a signal related thereto to the control system 18.Furthermore, the motor drive 60 may provide motor current, voltage,and/or torque information to the diagnostics and control system 66, forexample, wherein pump input power information may be calculatedaccording to the torque and possibly speed information.

The control system 18 also comprises a motor drive 60 providingthree-phase electric power from an AC power source 62 to the motor 16via the cables 50 in a controlled fashion (e.g., at a controlledfrequency and amplitude or prescribed PWM waveform) in accordance with acontrol signal 64 from the diagnostics and control system 66. Thediagnostics and control system 66 receives the process variablemeasurement signals from the suction pressure sensor 24, the dischargepressure sensor 40, the flow sensor 38, and the speed sensor 46,together with the setpoint 19 and/or other sensor signals, and providesthe control signal 64 to the motor drive 60 in order to operate the pumpsystem 12 commensurate with the setpoint 19, setup information 68,and/or a diagnostics signal 72 from a diagnostics system 70.

In this regard, the diagnostics and control system 66 may be adapted tocontrol the system 12 to maintain a desired fluid flow rate, outletpressure, motor (pump) speed, torque, suction pressure, or otherperformance characteristic. Setup information 68 may be provided to thediagnostics and control system 66, which may include operating limits(e.g., min/max speeds, min/max flows, min/max pump power levels, min/maxpressures allowed, NPSHR values, minimum/maximum motor temperatures, andthe like), such as are appropriate for a given pump 14, motor 16, andpiping and process conditions.

The diagnostics and control system 66 comprises a diagnostics system 70,which is adapted to detect and/or diagnose cavitation in the pump 14,according to an aspect of the invention. Furthermore, the diagnosticsand control system 66 selectively provides the control signal 64 to themotor drive 60 via a controller component 71 (e.g., which may implementone or more control strategies, such as proportional, integral,derivative (PID) control, or the like) according to the setpoint 19(e.g., in order to maintain or regulate a desired flow rate), setupinformation 68, and/or a diagnostics signal 72 from the diagnosticssystem 70 according to detected cavitation in the pump, wherebyoperation of the pumping system 12 may be changed or modified accordingto the diagnostics signal 72. The diagnostics system 70 as well as thecontroller 71 may be implemented in hardware, software, and/orcombinations thereof according to appropriate coding techniques in orderto implement the various aspects of the present invention.

The diagnostics system 70 may detect the existence of one or more actualor anticipated conditions associated with the pump system 12, such assystem component wear, degradation, failures, faults, or the like. Inaddition, the diagnostics system 70 may detect and diagnose processconditions associated with the system 12. For instance, the diagnosticssystem 70 may identify or detect cavitation in the pump 14, andadditionally diagnose the extent of such cavitation according topressure and flow data from the sensors 24, 40, and 38 (e.g., and pumpspeed data from the sensor 46), or alternatively from currentinformation from sensor 49, whereby the diagnostics signal 72 isindicative of the existence and extent of cavitation in pump 14.

FIG. 2 b illustrates a system for controlling a motorized system 90(e.g., motor, pump, combination thereof and/or plurality in combinationthereof). A sensing system 92 (e.g., sensor, plurality of sensors) sensevarious attributes (modalities) associated with the motorized system inoperation or while in a non-operating state. A diagnostic system system94 employs data from the sensing system 92 in connection with making adiagnosis of a state of the motorized system. Various aspects of thediagnostic system 92 are discussed herein in significant with respect todiagnostic system described regarding various embodiments of the subjectinvention. Accordingly, for sake of brevity and avoidance of redundancydiscussion of the various embodiments/application of the diagnosticsystem are omitted. A prognostic system 96 employs data from the sensingsystem 92 and/or diagnostic system 94 in connection with prognosing(e.g., determining, predicting, inferring) future states of themotorized system. The diagnostic system 94 and/or prognostic system 96can be implemented as a computer component. As used in this application,the term “computer component” is intended to refer to a computer-relatedentity, either hardware, a combination of hardware and software,software, or software in execution. For example, a computer componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution, a programand a computer. By way of illustration, both an application running on aserver and the server can be computer components. One or more computercomponents may reside within a process and/or thread of execution and acomputer component may be localized on one computer and/or bedistributed between two or more computers.

The present invention may also employ technologies associated withfacilitating inference and decision making under uncertainty andoptimization of expected utility and/or minimization of expected costs.Thus, statistical inference may be performed with models constructed byhand, from data with machine learning methods, or by a mixture ofmachine learning and human assessment. Such models can be used inconjunction with deterministic policies where depending on the context,an inferential rule or deterministic rule is used. A variety of machinelearning systems/methodologies (e.g., Bayesian learning methods thatperform search over alternative dependency structures and apply a score(such as the Bayesian Information Criteria, etc.) methods, Bayesianclassifiers and other statistical classifiers, including decision treelearning methods, support vector machines, linear and non-linearregression, expert systems and neural network representations, etc.) maybe employed to build and update inferential models. The prognosticsystem 96 can employ nonlinear training systems/methodologies, forexample, back-propagation, Bayesian, Fuzzy Set, Non Linear regression,or other neural network paradigms including mixture of experts,cerebellar model arithmetic computer (CMACS), Radial Basis Functions,directed search networks, and functional link nets. Moreover, theprognostic system 96 could employ various models as described hereinwith respect to the diagnostic systems, and it is to be appreciated thatsuch models or the like suitable for application with the prognosticsystem 96 are contemplated and intended to fall within the scope of thehereto claims.

A controller 98 employs information from either of the sensing system92, diagnostic system 94 and/or prognostic system to facilitatecontrolling the motorized system. With regards to the prognosticinformation from the prognostic system 96, the controller candynamically control/alter (e.g., slow down, speed-up, shut down,schedule shut-down) the state of the motorized system 90 to facilitatemaximizing utilization of the system 90. Thus, for example, if theprognostic information indicates that a future state of the system 90may be an overload condition, the controller 98 can take affirmativemeasures to address the anticipated overload condition. Thus, theprognostic data can be employed to mitigate, eliminate, or even avoid anundesirable operating condition of the system 90. Furthermore, theprognostic data can be employed to prolong life of the system, maximizeutilization of the system 90, and optimize preventive maintenance of thesystem.

Referring also to FIGS. 3-6, the diagnostics system 70 may comprise aneural network 80, an expert system 82, a data fusion component 84,and/or a fast Fourier transform (FFT) system 86 for generating afrequency spectrum 88, in order to provide the diagnostics signal 72according to one or more measured system attributes (e.g., via signalsfrom the sensors 24, 33, 35, 37, 38, 40, 46, 47, 48, and/or 49). System70 could include a pump model qualitative model (e.g., CNETS (causalmodel) or a combination of these). The measured attributes may also beprovided to the controller 71 from one or more of the sensors 24, 33,35, 37, 38, 40, 46, 47, 48, and/or 49 for performing closed loop controlof the system 12 in accordance with the setpoint 19, setup information68, and/or the diagnostics signal 72. Although the aspects of theinvention are described hereinafter within the context of employing aneural network 80, it is to be appreciated the invention may employother nonlinear training systems/methodologies, for example,back-propagation, Bayesian, Fuzzy Set, Non Linear regression, or otherneural network paradigms including mixture of experts, cerebellar modelarithmetic computer (CMACS), Radial Basis Functions, directed searchnetworks, and functional link nets. It is appreciated that sensors 24,33, 35, 37, 38, 40, 46, 47, 48, and/or 49 and other signals orcommunications among components may communicate, send data and/ortransfer signals through wireless communication or powerlinecommunication. It is appreciated that other sensors than the sensorsshown (24, 33, 35, 37, 38, 40, 46, 47, 48 and 49) may be used with thepresent invention.

The diagnostics and control system 66 thus comprises the controller 71operatively associated with the motorized system 12 to operate thesystem 12 in a controlled fashion via the control signal 64 to the motordrive 60, as well as the diagnostics system 70 operatively associatedwith the motorized system 12 and adapted to diagnose the health thereofaccording to one or more measured attributes (e.g., vibration, pressure,current, speed, and/or temperature), such as the signals from sensors24, 33, 35, 37, 38, 40, 46, 47, 48, and/or 49 in accordance with thepresent invention. It will be noted at this point that although theexemplary motorized system 12 includes a pump 14, that the diagnosticsand control system 66 may be employed in association with othermotorized systems (not shown) having a motor and a load, including butnot limited to valve, pump, conveyor roller, fan, compressor, and/orgearbox type loads. It is also be noted that the diagnostics system 70may effectively diagnose problems with other system components, such asthe motor drive 60, incoming power from source 62, or with othercomponents or elements of the system 12, such as sensor or A/D modules(not shown).

In accordance with the invention, the diagnostics system 70 providesdiagnostics signal 72 according to the health of the system 12 and/orcomponents therein (e.g., motor 16, drive 60, pump 14), wherein thecontroller 71 may advantageously provide control signal 64 to the drive60 according to the setpoint 19, setup information 68, and/or thediagnostics signal 72. As another application, the measured attribute orattributes may comprise vibration signals obtained from one or both ofthe sensors 37 and 48, and/or from other vibration sensors (e.g.,accelerometers) associated with the motor 16 or the pump 14. Forinstance, the diagnostics system 70 may obtain one or more motorvibration signals (e.g., from sensor 48), and diagnose the health of oneor more motor bearings, motor shaft alignment (e.g., lateral and/orangular alignment of the motor 16 with the pump 14 and/or misalignmentthereof), and/or of the motor mounting (e.g., via motor mounts 32)according to the measured vibration. In addition, it will be noted thatsuch diagnosis can be performed with no motor or pump sensors.

The diagnostics system 70 in this regard, may be adapted to performfrequency spectral analysis of the measured vibration signal from sensor48. Such spectral analysis may be performed, for example, via one ormore of the neural network 80 and the expert system 82, where thediagnostics signal 72 indicates the health of the motorized system 12according to frequency spectral analysis of the measured vibrationsignal. Alternatively or in combination, the diagnostics system 70 mayemploy the data fusion system 84 in order to derive one or morevibration signals from at least one sensor associated with the motorizedsystem 12. Thus, for example, where the vibration sensors 37 and/or 48are inoperative, data fusion techniques may be employed in accordancewith the invention, to derive vibration information from other availablesystem signals, such as via the current sensor 49.

The present invention may thus employ data fusion in situations in orderto take advantage of information fission which may be inherent to aprocess (e.g., vibration in the motor 16) relating to sensing a physicalenvironment through several different sensor modalities. In particular,one or more available sensing elements (e.g., sensors 24, 33, 35, 37,38, 40, 46, 47, 48, and/or 49) may provide a unique window into thephysical environment where the phenomena to be observed is occurring(e.g., in the motorized system 12 and/or in a system of which themotorized pumping system 12 is a part). Because the complete details ofthe phenomena being studied (e.g., detecting the operating state of thesystem 12 or components thereof) may not be contained within a singlesensing element window, there is information fragmentation which resultsfrom this fission process. These information fragments associated withthe various sensing devices may include both independent and dependentcomponents.

The independent components may be used to further fill out (or span) theinformation space and the dependent components may be employed incombination to improve the quality of common information recognizingthat all sensor data may be subject to error and/or noise. In thiscontext, data fusion techniques employed in the data fusion system 84may include algorithmic processing of sensor data (e.g., from one ormore of the sensors 24, 33, 35, 37, 38, 40, 46, 47, 48, and/or 49) inorder to compensate for the inherent fragmentation of informationbecause a particular phenomena may not be observed directly using asingle sensing element. Thus, the data fusion system 84 provides asuitable framework to facilitate condensing, combining, evaluating andinterpreting the available sensed information in the context of theparticular application. It will further be appreciated that the datafusion system 84 may be employed in the diagnostics and control system66 in order to employ available sensors to infer or derive attributeinformation not directly measurable, or in the event of sensor failure,and additionally, to detect sensor failure, and to continue to diagnoseand control system operation with failed sensor components.

Thus, the present invention provides a data fusion framework andalgorithms to facilitate condensing, combining, evaluating andinterpreting various sensed data. The present invention also facilitatesestablishing a health state of a system employing the diagnostics andcontrol system 66, as well as for predicting or anticipating a futurestate of the system 12 (e.g., and/or of a larger system of which themotorized pump system 12 is a part). As illustrated in FIG. 5, the datafusion system 84 may be employed to derive system attribute informationrelating to any number of attributes according to measured attributeinformation (e.g., from the sensors 24, 33, 35, 37, 38, 40, 46, 47, 48,and/or 49) in accordance with the present invention. In this regard, theavailable attribute information may be employed by the data fusionsystem to derive attributes related to failed sensors, and/or to otherperformance characteristics of the system 12 for which sensors are notavailable. Such attribute information derived via the data fusion system84 may be employed in generating the diagnostics signal 72, and/or inperforming control functions in the controller 71.

In another example, the measured attributes may comprise flow andpressure signals obtained from sensors 24, 38, and/or 40 associated withthe pump 14, wherein the diagnostics system 70 provides the diagnosticssignal 72 indicative of pump cavitation according to the measured flowand pressure signals. The invention thus provides for health indicationsrelating to component conditions (e.g., wear, degradation, faults,failures, etc.), as well as those relating to process or systemsconditions, such as blockage and/or cavitation in the pump 14. Thediagnostics system 70 may comprise a classifier system, such as theneural network 80, detecting pump cavitation according to the measuredflow and pressure signals, which may be provided as inputs to the neuralnetwork 80. The cavitation indication in the resulting diagnosticssignal 72 may further be employed to modify operation of the system 12,for example, in order to reduce and/or avoid such cavitation. Thus, anappropriate control signal 64 may be provided by the controller 71 tothe motor drive 60 in order to avoid anticipated cavitation, based onthe diagnostics signal 72 (e.g., and/or the setpoint 19), whereby theservice lifetime of one or more system components (e.g., pump 14) may beextended. The control signal 64 can further be provided to reducecavitation to a prescribed low level to meet process constraints and toextend machinery lifetime to a specific time horizon (e.g., to allow formission completion). Alternatively, controller 71 may provide the signal64 to slightly increase cavitation to that which is less damaging, tothe extent possible, in order to meet process (e.g., mission survival)needs and to ensure process/mission completion.

In another related example, cavitation (e.g., actual or suspected) inthe pump 14 may be detected via measured (e.g., or derived) currentsignal measurements, for example, via the sensor 49. The diagnosticssystem 70 in this instance may provide a diagnostics signal 72indicative of pump cavitation according to the measured current. Inorder to detect cavitation using such current information, thediagnostics system 70 may employ the neural network 80 to synthesize achange in condition signal from the measured current. In addition, thediagnostics system 70 may further comprise a preprocessing portion (notshown) operatively coupled to the neural network 80, which conditionsthe measured current prior to inputting the current into the neuralnetwork 80, as well as a post processing portion operatively coupled tothe neural network 80 to determine whether the change in conditionsignal is due to a fault condition related to the motorized system 12.In this regard, the post processing portion may comprise a fuzzy rulebased expert system, such as system 82. In addition, the diagnosticssystem 70 may detect one or more faults relating to the operation of thepump 14 and/or one or more faults relating to the operation of the motor16 driving the pump 14 according to the measured current.

Other faults may be detected and diagnosed using the diagnostics andcontrol system 66 of the invention. For instance, the diagnostics system70 may be adapted to obtain a space vector angular fluctuation from acurrent signal (e.g., from the current sensor 49) relating to operationof the motor 16, and further to analyze the space vector angularfluctuation in order to detect at least one fault in the motorizedsystem 12. Such faults may include, for example, stator faults, rotorfaults, and/or an imbalance condition in the power applied to the motor16 in the motorized system 12

In this situation, the diagnostics system 70 may obtain a current signalassociated with the motor 16 from sensor 49, and calculate a spacevector from the current signal. The diagnostics system 70 determines aspace vector angular fluctuation from the space vector, and analyzes thespace vector angular fluctuation in order to detect one or more faultsassociated with the motor 16. For instance, first, second, and thirdphase current signals associated with the motorized system 12 may besampled in order to obtain the current signal, and corresponding first,second, and third phase space vectors may be computed in the diagnosticssystem 70.

A resulting space vector may then be calculated, for example, by summingthe first, second, and third phase space vectors. The diagnostics system70 may then compare the space vector with a reference space vector,wherein the reference space vector is a function of a constant frequencyand amplitude, and compute angular fluctuations in the space vectoraccording to the comparison, in order to determine the space vectorangular fluctuation. The diagnostics system 70 then performs frequencyspectrum analysis (e.g., using the FFT component 86) of the space vectorangular fluctuation to detect faults associated with the motorizedsystem 12. For example, motor faults such as rotor faults, statorfaults, and/or unbalanced supply power associated with the pump motor 16may be ascertained by analyzing the amplitude of a first spectralcomponent of the frequency spectrum at a first frequency, wherein thediagnostics system 70 may detect fluctuations in amplitude of the firstspectral component in order to detect one or more faults or otheradverse conditions associated with the motorized system 12.

In this regard, certain frequencies may comprise fault relatedinformation, such as where the first frequency is approximately twicethe frequency of power applied to the motor 16. Alternative togenerating a full spectrum, the diagnostics system 70 may advantageouslyemploy a Goertzel algorithm to extract the amplitude of the firstspectral component in order to analyze the amplitude of the firstspectral component. The diagnostics signal 72 indicating such motorfaults may then be employed by the controller 71 to modify operation ofthe pumping system 12 to reduce or mitigate such faults. Following acontrol modification, the amplitude of the first spectral component isre-analyzed to further support (e.g., or deny) the hypothesized faultand mitigation scheme (e.g., cavitation).

Referring now to FIG. 7, the processing performed on vibration and othersampled data from one or more of the system sensors 24, 33, 35, 37, 38,40, 46, 47, 48, and/or 49 in the diagnostics system 70 may comprisedemodulation. One demodulation technique, sometimes referred to asenveloping, may be performed by the system 70 in order to synthesizesampled digital attribute data 100 into a form usable for detecting anddiagnosing component degradation and process conditions in the system12. The demodulation is illustrated in FIG. 7 in association withvibration data (e.g., from one of the sensors 37 or 48). The digitalvibration data 100 enters the diagnostics system 70 and passes through aband pass filter 102, which removes frequencies outside the scope ofinterest and within the dynamic range of the system 70 to form afiltered signal 104.

The filtered signal 104 passes through a rectifier 106, for example adiode, which forms a rectified signal 108. The rectified signal 108passes through a low pass filter 110 which removes the high frequenciesto form a relatively low frequency signal 112. The low frequency signal112 is passed through a capacitor 114 to produce a demodulated signal116. A fast Fourier transform (FFT) is performed on the demodulatedsignal 116 by FFT operator 118 (e.g., such as FFT component 86 of FIG.6) in order to produce a vibration spectrum 120, for example, usingcommercially available fast Fourier transform software such as MATLAB byThe Math Works. The FFTs of the vibration signal data are discretizedover N number of points to facilitate processing, such as where N=2,048(e.g., where N=2^(K), and K is an integer), however, it will beappreciated that the FFTs of each signal may be discretized over anysuitable number of such points. The vibration spectrum 120 may then beanalyzed by a host processor in the control system 18 in order todetermine the health of the motor 16 or other component in the motorizedsystem 12.

Although the demodulation has been described with respect to obtainingFFTs of vibration or other signals, other suitable techniques may beemployed. For example, wavelet transforms may be taken of the sensordata. One advantage to using the wavelet transform is that the totalsize of the transform is a compact representation of the original signaland will require considerably less storage space than the originalsignal. The wavelet representation may be diagnosed directly on theoriginal signal reconstructed from the wavelet presentation.Alternatively, where particular spectral components are of interest,techniques such as the Goertzel algorithm may be used to obtain suchcomponents and the diagnosis without the need to generate an entirefrequency spectrum. Furthermore, such spectral analysis of vibrationdata to detect and diagnose bearing faults, may further take intoconsideration motor (e.g., pump) speed information (e.g., from sensor46) and setup information 68 (e.g., bearing geometry data) in order tosuitably interpret the FFT and detect defects and isolate particularbearing faults.

Referring also to FIG. 8, the diagnostics system 70 may further comprisea pre-processing component 202 receiving speed, pressure, and flow datafrom the sensors 46, 24, 40, and 38, respectively, which provides one ormore attributes 204 to the neural network 80, wherein the attributes 204may represent information relevant to cavitation in the pump 14. Theattributes 204 may be extracted from the measured pressure, flow, and/orspeed values associated with the pumping system 12, and used tocharacterize pump cavitation by the neural network 80. The neuralnetwork 80, in turn, generates a diagnostics signal 72 which maycomprise a cavitation classification 206 according to another aspect ofthe invention. The neural network classifier 80 thus evaluates datameasured in the motorized pumping system 12 (e.g., represented by theattributes 204) and produces a diagnosis (e.g., diagnostics signal 72)assessing the presence and severity of cavitation in the system 12. Theneural network 80 in this regard, may employ one or more algorithms,such as a multi-layer perception (MLP) algorithm in assessing pumpcavitation.

As illustrated further in FIG. 9, the diagnostics signal 72 output bythe classifier neural network 80 is indicative of both the existence andthe extent of cavitation in the pumping system 12. For instance, theexemplary signal 72 comprises a classification 206 of pump cavitationhaving one of a plurality of class values, such as 0, 1, 2, 3, and 4. Inthe exemplary classification 206 of FIG. 9, each of the class values isindicative of the extent of cavitation in the pumping system 12, whereinclass 0 indicates that no cavitation exists in the pumping system 12.The invention thus provides for detection of the existence of cavitation(e.g., via the indication of class values of 0 through 4 in thediagnostics signal 72), as well as for diagnosis of the extent of suchdetected cavitation, via the employment of the neural network classifier80 in the diagnostics system 70.

Referring now to FIG. 10, an exemplary neural network 80 comprises aninput layer 210 having neurons 212, 214, 216, and 218 corresponding tothe suction pressure, discharge pressure, flow rate, and pump speedsignals, respectively, received from the sensors 24, 40, 38, and 46 ofthe pumping system 12. One or more intermediate or hidden layers 220 areprovided in the network 80, wherein any number of hidden layer neurons222 may be provided therein. The neural network 80 further comprises anoutput layer 230 having a plurality of output neurons corresponding tothe exemplary cavitation classification values of the class 206illustrated and described hereinabove with respect to FIG. 9. Thus, forexample, the output layer 230 may comprise output neurons 232, 234, 236,238, and 240 corresponding to the class values 0, 1, 2, 3, and 4,respectively, whereby the neural network 80 may output a diagnosticssignal (e.g., signal 72) indicative of the existence as well as theextent of cavitation in the pumping system (e.g., system 12) with whichit is associated. For example, each of the output neurons 232, 234, 236,238, and 240 may output a unique value indicating the degree ofcertainty that the associated cavitation class is present in the system12.

In this regard, the number, type, and configuration of the neurons inthe hidden layer(s) 220 may be determined according to design principlesknown in the art for establishing neural networks. For instance, thenumber of neurons in the input and output layers 210 and 230,respectively, may be selected according to the number of attributes(e.g., pressures, flow, speed, etc.) associated with the system 70, andthe number of cavitation classes 206. In addition, the number of layers,the number of component neurons thereof, the types of connections amongneurons for different layers as well as among neurons within a layer,the manner in which neurons in the network 80 receive inputs and produceoutputs, as well as the connection strengths between neurons may bedetermined according to a given application (e.g., motorized system) oraccording to other design considerations. In addition, the neuralnetwork may have only one output, the value of which indicates thecavitation class for the system 12.

Accordingly, the invention contemplates neural networks having manyhierarchical structures including those illustrated with respect to theexemplary network 80 of FIG. 10, as well as others not illustrated, suchas resonance structures. In addition, the inter-layer connections of thenetwork 80 may comprise fully connected, partially connected,feed-forward, bi-directional, recurrent, and off-center or off-surroundinterconnections. The exemplary neural network 80, moreover, may betrained according to a variety of techniques, including but not limitedto unsupervised learning, reinforcement learning, and supervised (e.g.,back propagation), wherein the learning may be performed on-line and/oroff-line. Furthermore, the training of the network 80 may beaccomplished according to any appropriate training laws or rules,including but not limited to Hebb's Rule, Hopfield Law, Delta Rule,Kohonen's Learning Law, and/or the like, in accordance with the presentinvention. In addition, although one or more aspects of the presentinvention are primarily described herein in the context of employing aneural network, it is to be appreciated the invention may employ othernonlinear training systems and/or methodologies (e.g., example,back-propagation, Bayesian, Fuzzy Set, Non Linear regression, or otherparadigms including mixture of experts, cerebellar model arithmeticcomputer (CMACS), Radial Basis Functions, directed search networks,functional link nets, and the like).

Referring now to FIG. 11, the diagnostic system 70 may further performone or more types of signal conditioning on the measured attributesignals from the system sensors 24, 33, 35, 37, 38, 40, 46, 47, 48,and/or 49. As an example, one or more motor currents may be sampled, forexample, from the sensor 49, and processed accordingly, in order todetect and/or diagnose cavitation conditions in the pump 14. In thisregard, the current sensor 49 may comprise a current transformerproviding a current signal to a signal conditioning system 306, which inturn provides a conditioned current signal 320 to an analog to digital(A/D) converter 272. Thereafter, a digitized current signal is providedfrom the A/D 272 to a processor 290 in the diagnostics and controlsystem 66 for further processing, as illustrated in FIG. 11.

FIG. 11 illustrates the functional events or acts that the current datafrom the motor 16 driving the pump 14 is subjected to for pump conditiondiagnostics. After the current data from current transformer 49 isconditioned by signal conditioning circuit 306, it is converted fromanalog data to digital data by the A/D converter 272, so that it can befurther processed by processor 290. Processor 290 first performs theacts of computing Fast Fourier Transforms 326 of the current data. Theprocessor 290 controls the signal sampling and digitizing rate as wellas any buffering of the digitized signals that might be needed. Thisdata collection rate should be selected to provide sufficient data uponwhich the processor 290 can generate a comprehensive frequency spectrumof the motor current signal suitable for analysis using commerciallyavailable Fast Fourier Transform software, such as for example MATLAB byThe Math Works.

The spectral analysis basically involves the application of a ‘HanningWindow’ to the time-domain data, calculation of power spectrum from eachset of the windowed time-domain data for the specified number of sets,and then finding the average spectrum using the Welch method. A flowchart of the general scheme is shown in FIG. 13. The output spectracorresponding to the A/D current signal, is stored into a designatedoutput file for future use. The parameters for the A/D data acquisition,such as the number of sets (NumSets), number of samples per set (NumPts)and the sampling rate are selected to be 8, 8192 and 4096 Samples/secrespectively, however it will be appreciated that any appropriate valuesfor these parameters may be employed in accordance with the presentinvention. These parameters yield a frequency resolution of 0.5 Hz and abandwidth of 0 to 2048 Hz in the frequency spectra. The time-domain dataconsists of a contiguous record of 65536 data values collected over 16seconds, which is then divided into eight equal sets. The noisesmoothing is of a satisfactory level after the averaging of eightconsecutive spectra. A Hanning window may be employed for the windowingpurpose because of its ability to reduce the leakage effect to aminimum.

The processor 290 then finds and stores fault signatures relating to theoperation of the pump in 328. FIGS. 14 a-14 f show how pump faults canbe detected comparing frequency spectrums of motor current data relatingto normal and fault conditions. FIGS. 14 a-14 b show both a frequencyspectrum of a centrifugal pump in a normal condition 350 and acavitation condition 352. FIG. 14 b illustrates a magnified image ofFIG. 14 a within a limited frequency range, wherein a difference in thespectrums can be seen during cavitation. Furthermore, FIGS. 14 c and 14d show both a frequency spectrum of a centrifugal pump in a normalcondition 354 and a seal leakage condition 356 resulting in two-phaseflow, while FIGS. 14 e and 14 f illustrate both a frequency spectrum ofa centrifugal pump in a normal condition 358 and a faulty impellercondition 360. It should be appreciated that other fault conditions suchas blockage will also lead to different frequency spectrumcharacteristics of the current data of the motor driving the pump.Furthermore, different types of pumps will exhibit different types offaults with associated different spectrum characteristics. For example,pump faults could include cavitation, blockage, two-phase flow, impellerwear, impeller damage, the impeller impacting with the casing, a pumpout of balance, corrosion, surge/hammer, pump bearing defects, or othertypes of faults.

In one aspect of the invention, the processor 290 could access a table,such as the one shown in FIG. 14 g. The table 400 is shown which theprocessor 290 accesses when performing signature analysis to diagnosethe health of the pump 14. The table 400 includes current amplitude data(A₀ thru A_(Z)) over a range of frequencies (f₀ thru f_(n)). The table400 is stored in memory in the diagnostics and control system 66, so asto be easily accessible by the processor 290. The table 400 includesvarious health states of the pump shown generally at 402, whichcorrespond to current amplitudes over the frequency range f₀ thru f_(N).For example, referring to the row identified by reference numeral 404,when the current amplitudes are A₂₃₄ at f₀, A₂₇ at f₁, A₄₇₈ at f₂, A₂₄at f₃, A₁₂₇ at f₄, . . . , A_(Q) at f_(n), the table 400 indicates thatthe pump 14 has a “pump fault 6”. The “pump fault 6” could be acavitation fault or a variety of other pump related faults. As will beappreciated, the table 400 can store N number of current signaturescorresponding to various health states of the pump 14, which theprocessor 290 can employ to diagnose the health of the pump 14.

It will be noted that the amplitude values need only be sufficientlynear the prescribed amplitude values in the table 400. Alternatively,the pump fault classification 402 could be assigned to a set of sampledfrequencies corresponding to the row it is closest to. In this regard,closeness can be defined by any of the standard metrics in n-space(e.g., n-dimensional Euclidean distance). It will be further noted atthis point that a diagnosis can be several concurrent faults, such ascavitation with simultaneous faulty bearings (e.g., bearing fault causedby cavitation which is continuing).

Furthermore, certain discriminating fault attributes may be extractedfrom the frequency spectrum of motor current, which relate to certainfault conditions of the pump. Typical attributes such as motor slip andnoise directly relate to degree of cavitation. An algorithm performed byprocessor 290 can be seen in FIG. 15, which evaluates the components ofthe frequency spectrum and derives certain attributes for a centrifugalpump. The attributes shown in FIG. 15 are slip, FsAmp, SigAmp, Noise_1,Noise_2, Noise_3, Noise_4 and Noise_5, which form the fault signaturefor the centrifugal pump. For example, if FsAmp is low and slip is lowthere is fault condition of severe blockage. Other types of faults canbe found, such as cavitation and faulty impeller by evaluating the aboveattributes. The attributes then may be subjected to the act ofpreprocessing 330 to be acceptable by the neural network 80.

In some cases may be desirable to preprocess the fault signature databefore it is being used to train or design a decision module based onANN paradigms. The preprocessing can be divided into three tasks,namely, elimination of outliers, scaling, and bifurcation of data intotraining and testing sets. Elimination of outliers is concerned withdetecting any such data pattern that has one or more attributes whichseem to have an extraordinarily large or small values compared to theallowed or expected range for that attribute(s). Such data patterns areknown as outliers, which could be generated due to errors during datacollection or fault signature formation or due to noise. Elimination ofsuch data patterns facilitates proper utilization of the data set fordesigning the network. The adverse effects caused by not eliminatingoutliers are compression of the useful range of a given attribute duringscaling and causing difficulties for the network in converging to afinal solution, and error in the final solution due to warping of theattribute space in order to accommodate extreme values.

The fault signature, which is an array of real values, is known as ananalog data pattern, analog exemplar, or feature vector in the field ofneural networks. Although it is possible to take the attributesgenerated in the previous section and apply them directly as inputs to aneural network, it is practically more beneficial in terms of simplicityof the designed network and in terms of the computational load on theprocessor, to scale the attributes in such a way that each of theattributes has similar boundaries such as {0, 1} or {−1, 1}. Forexample, in the pump condition monitoring data patterns, the slipattribute would have a value on the order of 10⁻² while the FsAmpattribute can have values greater than 10³ and the possible range oftheir values can also differ greatly. Using such a data set directly,without scaling, in conjunction with a neural network would lead tolarge values of network parameters (e.g., weight vectors) and the timetaken for completely training the network or designing the network wouldbe high. The simplest and most widely used scaling method is the linearscaling method. Alternatively sigmoidal scaling can be used.

Returning to FIG. 11, the processor 290 then transmits the faultsignature to neural network 80 after 330. Neural network 80 could be amulti-iterative, supervised learning algorithm such as feed forward,back propagation network. This network could be trained and used with afully labeled data set corresponding to all possible operatingconditions. However, the application of unsupervised ANN techniques thatcould learn on-line (even in a single iteration) may be desirable. Also,the ability to perform incremental learning such as provided in theproposed unsupervised neural network is desirable. Processor 290 can beprogrammed to perform the necessary post processing of the fault data in332 and provide that information in the diagnostics signal 72. It shouldbe appreciated that the functions of diagnosing the motor current datacontaining pump fault information can be completely performed by thediagnostics and control system 66.

FIG. 12 illustrates an implementation of the present invention whereinthe diagnostics and control system 66 performs the signal conditioning380, A/D conversion 382, spectral analysis 384, formation of the faultsignatures 386 and pre-processing 388. The diagnostics and controlsystem 66 may also include a decision module 390 which may be comprisedof neural network 80 and post processing 332.

Neural network 80, which may comprise an Artificial Neural Network(ANN), will now be discussed with regards to another implementation inaccordance with the present invention. ANNs are a set of algorithms orprocessing acts that can be used to impart the capabilities such asgeneralization and pattern recognition or pattern classification to astandard computer system. These algorithms can learn to recognizepatterns or classify data sets by two methods, known as supervisedlearning and unsupervised learning. Considering the block diagram shownin the FIG. 16, in which an ANN is represented by a ‘square block’ 410,the ANN is supervised if the network is provided with a set of inputpatterns 412 along with their designated outputs 414, and the networklearns by changing its internal parameters (e.g., neuron interconnectionweights) in such a way that it produces the corresponding designatedoutput pattern for a given input pattern, or sufficiently close to thedesignated output pattern. The ANN is known as unsupervised if thenetwork chooses its own output class for a given input pattern withoutany external supervision or feed-back. The unsupervised ANN willautomatically define new classes if new input patterns are sufficientlydifferent from patterns previously seen.

The ANN structure basically contains processing nodes called neuronsarranged in two or more layers. The nodes are extensively interconnectedtypically in a feed forward manner through connections and associatedinterconnection weights. For a pattern recognition application, theinput pattern could be a digitized image of an object to be recognizedand the output could be the same image (e.g., with reduced noise), or itcould be a class representation of the image. For a condition monitoringapplication such as for motorized systems, the input pattern may includepreprocessed fault signature data derived from the stator currentspectra, and the output is a class assignment for one of variouspossible fault conditions. There are various architectures and learningschemes for the ANNs and some of the ANNs may be more suitable for useas decision makers for condition diagnostics applications than others.

It has been found that one-shot unsupervised ANN paradigms may be moresuitable for the development of condition monitoring system thansupervised ANN paradigms. Furthermore, a monitoring system based on anunsupervised neural network that can learn about the conditions of aprocess plant from a single pass of the training data, can provide abetter solution for detecting new plant conditions. Supervised networksmust be trained off line, and thus have a fixed set of variables andcannot give a valid output upon receipt of a new condition. A blockdiagram of a one such one-shot unsupervised ANN network known as theAdaptive Resonance Theory (ART) is shown in FIG. 17.

ART has two layers namely the input (or comparison) layer 440 and output(or recognition) layer 442. These layers are connected together, unlikethe other networks discussed above, with feedforward (denoted by W) aswell as feedback (denoted by T) connections. The neurons of the outputlayer also have mutual connections useful for lateral inhibition (notshown in the Figure). The signals Control-1 and Control-2 areresponsible for controlling the data flow through the input and outputlayers, 440 and 442, respectively. The reset circuit is responsible fordetermining the effectiveness with which a winning output neuronrepresents the input pattern. It is also responsible for resetting theineffective neurons and designating a new neuron for representing agiven input pattern. The training of the ART network can be done ineither a fast learning mode or in a slow learning mode. The fastlearning mode allows the network's feed forward weights to attain theiroptimum values within few learning cycles (epochs) while the slowlearning mode forces the weights to adapt over many epochs. The fastlearning mode can be used to train the network even in a single epoch.This is appropriate for the present case because the salient features ofthe problem domain are already well defined in the fault signatures.

FIG. 18 illustrates the ART-2 architecture, which is essentially thesame as the ART architecture except that the input layer of ART-2 hassix sublayers (w, x, v, u, p and q) which are designed to enhance theperformance of the network to cope with the continuously varying realinput data. The additional sublayers incorporate the effects of featureenhancement, noise suppression, sparse coding and expectation from therecognition layer. The reset function is incorporated by an additionalsublayer (r) containing the same number of nodes as are in the sublayersof the input layer.

Another useful unsupervised ANN algorithm applicable to the presentinvention is the Associative List Memory (ALM) paradigm. AssociativeList Memory (ALM) is an autoassociative memory capable of handling longbit strings (up to 10⁴ bits) of data. ALM's learning mechanism is basedon unsupervised training technique and can learn within a single epoch.It has been successfully applied to reduce and classify the dataproduced by satellites and the results have shown that ALM is comparableor better than other associative memories such as the Sparse DistributedMemory for this application. It provides direct access to the learnedexemplars and can be implemented on a low-cost computing platform (e.g.,a 16 bit microprocessor). With suitable modifications ALM can be usedfor classifying analogue inputs (e.g., pump fault signature data). Itsone-shot learning capability, simple algorithm structure, andincremental learning capability make it a suitable choice forimplementing a stand-alone decision module for pump diagnostics.

In a typical commercial or industrial motorized system, it may not bepossible to generate a dataset containing all possible operatingconditions or faults. Simple linear scaling of the available data maynot ensure that any future data, which might occur due to a new plantoperating condition, would lie within the same initial limits. Hence anadaptive preprocessing scheme may be employed in accordance with theinvention, which adaptively updates the maximum and minimum values ofeach attribute so that the entire database is maintained to have valuesbetween 0 and 1. This increases the computational task, but thepotential advantages of the generic condition monitoring schemeout-weigh this minor drawback. Output generated by an unsupervisedneural network needs to be post processed in order to obtain usefulclassification information. In the classification applications using theunsupervised ANN paradigms, usually the domain expert (a human expert,having knowledge about the problem) examines the output classes assignedby the ANN in conjunction with system details such as which of the inputpatterns has activated which of the output entries, the class to which agiven input actually belongs etc., and then assigns the classrepresentation to each of the output entries. The above procedure issuitable for applications such as classification of satellite picturesetc., but may not be suitable for on-line condition monitoringapplications.

Without the above mentioned post processing, an unsupervised ANN baseddecision module for the condition monitoring applications, can onlysignal the formation of a new output entry indicating that a possiblenew plant condition has occurred, but it cannot diagnose the newcondition to be a particular case, such as a new normal condition,cavitation etc. It is, however, possible that an intelligent postprocessor based on expert system rules, can be used to assign the classrepresentation to each of the output entries formed by the unsupervisedneural networks such as ART and ALM. The attributes of the faultsignatures obtained from the stator current of the motor are derivedbased on the physical interpretation of the functioning of the pumps andpumping systems. This means that the variation of each of the attributesare associated with the physical status of the pump. Thus, it ispossible to correlate the attribute representing the pump condition withrules such as ‘IF attribute-1 is high and attribute-2 is high THEN thepump condition is cavitation’, etc. An expert system based on suchgeneric rules can be used to assign the class representation to theoutput entries formed by the unsupervised ANN paradigm so that thedecision module produces a meaningful output without any external humansupervision.

Referring to FIG. 19, a condition monitoring system is provided thatutilizes a stand-alone decision module 470 within the diagnostics andcontrol system 66. The stand-alone decision module 470 includes hardwareand/or software that performs an adaptive preprocessing act, an ALMneural network and software that utilizes a Fuzzy Expert System in apost processing act. To exploit the potential advantages of one-shotmethods as decision modules for the on-line condition monitoringschemes, a flexible preprocessing method is employed which adaptivelypreprocesses the incoming data to bind its attribute values to thepredefined limits of 0 to 1, by using pre-specified maximum and minimumlimits, which can be easily defined by the domain expert, who hasknowledge of the equipment (type of motor, pump etc.) and the plant(flow rate, differential pressure head etc.).

Where attribute values are not predictable, limits are set based onobservation of the range encountered during measurement. These initiallimits act as maximum and minimum boundaries for calculating scalingfactors to preprocess the incoming data as long as the incoming datapatterns fall within range. When any of the attributes of the incomingdata patterns has a value beyond these limits then the maximum andminimum boundaries are expanded to include the present pattern and newscaling factors are obtained. Whenever such a change of maxima or minimaboundaries occurs, the neural network paradigm that uses thepreprocessed data may be updated to account for the new changes in thescaling factors and also the previously accumulated data base may needto be rescaled using the new scaling factors based on how much the newboundaries have expanded.

In the case of the ART-2 algorithm, the feed-forward and feed-backweight vectors may be rescaled to account for the new changes, and foran ALM the memory entries themselves rescaled. Although the proposedadaptive preprocessing scheme will work without defining any maximum andminimum limits beforehand, it is faster and more efficient to specifyand utilize initial expected limits. It should be appreciated that thepresent invention could employ operator input training techniques. FIG.20 illustrates a flow-chart of the functional description of theadaptive preprocessing scheme 472. The expected maxima and minima foreach attribute are first derived by analyzing the normal condition dataand the other relevant pump/plant details, ratings and physical system.It should be noted that in some circumstances it may not be necessary toemploy preprocessing and postprocessing techniques.

A block diagram of a Fuzzy Rule Based Expert System (FRBES) 476 is shownin FIG. 21. It comprises three modules namely, a Fuzzy Rule Base 500, aFuzzy Inference Engine 502 and a User Interface 504. The Fuzzy Rule Base500 consists of Fuzzy type ‘IF-THEN’ rules, while the Fuzzy InferenceEngine 502 is the core logic program which includes details of the Fuzzysystem such as the number and types of membership functions of inputsand outputs, their range, method of defuzzification, etc. The UserInterface 504, in a production-level expert system is capable ofquerying the user for additional information in case the expert systemis unable to reach a conclusion with the existing information in itsdata base.

As can be seen in FIG. 22, the decision module 470 can be integrated asa stand-alone decision module where the input can be received both bythe Adaptive Preprocessor 472 and the FRBES 476. Bidirectionalcommunication is set up between the ALM Neural Net 474 and the FRBES476. The ALM may receive new classification information which may beentered from the user interface portion of the FRBES 476. The ALM NeuralNet 474 then generates the appropriate output pattern to a userinterface. The rule base of the FRBES in the present example comprisesfifteen generic rules formed on the basis of the generic knowledge aboutthe behavior of the attributes with respect to a given plant condition.These rules are listed in FIG. 23. The MATLAB source code correspondingto the FRBES described above can be implemented using MATLAB's FuzzyLogic Tool-Box. It should be appreciated that the Adaptive Preprocessor472, ALM paradigm 474, and the FRBES 476 could be implemented inseparate stand-alone hardware modules or in a single integrated hardwaremodule within the system 66. Furthermore, separate processors andsoftware programs could be used or a single processor and softwareprogram could be used to run the Adaptive preprocessing and FRBES acts.It should be appreciated that the stand-alone decision module 470 can beused to diagnose the operating condition of the pump system 12 (e.g.,cavitation), the health or condition of the pump 14 (e.g., impellerdamage), and also the condition of the motor 16 driving the pump 14(e.g., rotor bar damage).

Turning now to FIG. 24, the diagnostics and control system 66 is shownas part of a system 530 where the diagnostics and control system 66performs classical current signature analysis on a plurality of motors230 _(A)-230 _(D). The motors 230 _(A)-230 _(D) drive pumps 232 _(A)-232_(D). Although only four motors and four pumps are shown as part of thesystem 530, it will be appreciated that virtually any number of motorsand pumps may be employed in this system 530. The motors 230 _(A)-230_(D) are each individually tied to the diagnostics and control system66. The system 66 may include motor starters to start and stop themotors along with circuit breakers to protect the electric motors andelectric wiring. Each respective motor 230 _(A)-230 _(D) has currentsensor(s) coupled to its respective lead wire(s) to obtain currentsignal data. The digitized current signal data 320 for each motor 230_(A)-230 _(D) is applied to a channel interface 532. The channelinterface 532 includes a separate channel for each motor of the system530. The channel interface 532 is coupled to the diagnostics and controlsystem 66.

The diagnostics and control system 66 cycles through each channel atpredetermined intervals to obtain current data with respect to eachmotor 230 _(A)-230 _(D). The diagnostics and control system 66 thenessentially carries out the same acts as described above with referenceto FIG. 12 as to performing classical fault signature analysis todetermine the operating condition of the pumps 232 _(A)-232 _(D). Theadvantage of this system 530 is that it allows for the analyzing aplurality of pumps by a single diagnostics and control system 66. Inthis way, a user of the diagnostics and control system 66 could monitorand analyze every motor or machine within a facility from a singlelocation.

Although the present invention has been described with respect toobtaining Fast Fourier Transforms of the current signals, it should beappreciated that other suitable techniques may be employed. For example,wavelet transforms may be derived from the current data. FIG. 25 showsreplacing the act 326 (FIG. 11) of computing the Fast FourierTransformation of digital conditioned signal 320 with computing thewavelet of the conditioned signal 320. All that is required forperforming a wavelet transform is an appropriate set of analysis andsynthesis filters. By using the wavelet transform with the neuralnetwork 80, a much smaller, compact, training set may be employed, whichstill enables the present invention to correctly classify the operatingstate of the motor pump system. Furthermore, original signal informationfrom the wavelet coefficients as may be reconstructed if needed in thefuture. This approach also affords for a pseudo frequency domain andtime domain analysis of the signal data. Such a combination involvesrelative simplicity of implementation while affording great flexibilityin accommodating a broad range of signal types and noise levels.

It should be appreciated that the present invention employs techniquesthat show direct application to machinery diagnosis using a variety oftechniques, algorithms and models. These techniques could be readilyexpanded to include pump hardware and pump process protection viaautomatic shutdown, failure prediction/prognostics, corrective actionrecommendations, monitor and control energy usage and to ensure EPA andsafety guidelines are complied with using monitoring and archival datastorage. It should also be appreciated that the techniques provided inthe present invention could be applicable to a broad range of pumps(e.g., centrifugal, positive displacement, compressors, vacuum pumps,fans, ventilation systems, etc.).

In order to further illustrate the various aspects of the invention,FIGS. 26-44 and the following description are provided, includingexemplary experimental and simulated results showing the diagnosticcapabilities according to the invention. The space vector angularfluctuation technique (SVAF), provides significant advantages over prioranalytical methodologies, such as the zero cross times method (ZCT) formotor failure prediction. The ZCT method employs zero crossing times ofthree phase motor current waveforms as data for spectral analysis ofinduction motor current. The inventors of the present invention havefound that fluctuations in the angle of the space vector holdinformation on motor condition, and that when these are analyzed bymeans of FFT or other frequency spectral analysis techniques, diagnosticindices for stator and rotor faults can be defined. The main drawback inthe ZCT method was limited sampling frequency, whereas the SVAF methodovercomes this limitation, thus giving more reliable diagnostic data,since aliasing effects are removed.

The ZCT method of induction motor fault detection measures times t_(i)at which the three phase currents cross through zero. A series of datavalues is derived as the time difference between pairs of adjacent zerocrossing times minus the expected 60 degree time interval ΔT between twozero crossings. For a three phase system with six zero crossings permains cycle, this produces six samples per supply cycle with the datagiven byδt _(i) =t _(i) −t _(i−1) −ΔT,  (1)with ΔT=1/300 sec for a 50 Hz supply.

Since the phase lag angle of motor current behind the supply voltagevaries with load, it follows that fluctuations in load or speed from anycause will be encoded as modulation of the ZCT data values δt_(i). Thesedata values represent the fluctuations in the zero crossing times, andin an ideal system at constant speed, would all be equal to zero. Hence,only the fluctuations in load are encoded. Because there are six zerocurrent crossings in each supply cycle in a typical three phase system,the ZCT sampling is fixed at only six samples per cycle. Hence thesampling frequency of the ZCT signal is 300 Hz with a 50 Hz supply,giving a range of frequencies covering only (0-150) Hz in a ZCTspectrum.

The SVAF methodology according to the present invention is not solimited in sampling rate, and hence aliasing may be overcome orminimized, which heretofore has been problematic in association with theZCT technique. Thus, the invention provides for deriving sampled datafrom the rotational motion of the space vector representing the threephase currents, instead of just from the zero crossings. The balancedstator currents of amplitude I, phase angle φ and angular frequencyω_(s) for a symmetrical three phase winding are represented by thefollowing equation (2):

$\begin{matrix}{\begin{bmatrix}{i_{a}(t)} \\{i_{b}(t)} \\{i_{c}(t)}\end{bmatrix} = {{I\begin{bmatrix}{\cos\left( {{\omega_{s}t} + \phi} \right)} \\{\cos\left( {{\omega_{s}t} + \phi - {2\;\pi\text{/}3}} \right)} \\{\cos\left( {{\omega_{s}t} + \phi - {4\;\pi\text{/}3}} \right)}\end{bmatrix}}.}} & (2)\end{matrix}$These stator currents define a space vector as the sum of space vectorsof individual phases given by equation (3):{right arrow over (i)} _(s)= 3/2└i _(a)(t)+ai _(b)(t)+a ² i_(c)(t)┘,  (3)where a=e^(j2π/3) is a space operator.

For a balanced, symmetrical, steady state system, this vector rotates inthe space vector plane with constant amplitude in the positive directionat synchronous speed. Its locus in the space vector plane is a perfectcircle, as shown in (4):{right arrow over (i)} _(s) =Ie ^(jφ) ·e ^(jω) ^(s) ^(t)  (4)

However, for a system, with presence of any other additional componentcaused by either stator fault, rotor fault or other unbalance, ofamplitude I_(comp.), angular frequency ω_(comp.) and phase angleφ_(comp.), the three phase currents may be written as in (5).

$\begin{matrix}{\begin{bmatrix}{i_{a}(t)} \\{i_{b}(t)} \\{i_{c}(t)}\end{bmatrix} = {{I\begin{bmatrix}{\cos\left( {{\omega_{s}t} + \phi} \right)} \\{\cos\left( {{\omega_{s}t} + \phi - {2\;\pi\text{/}3}} \right)} \\{\cos\left( {{\omega_{s}t} + \phi - {4\;\pi\text{/}3}} \right)}\end{bmatrix}} + {I_{{comp}.}\begin{bmatrix}{\cos\left\lbrack {{\omega_{{comp}.}t} + \phi_{{comp}.}} \right\rbrack} \\{\cos\left\lbrack {{\omega_{{comp}.}t} + \phi_{{comp}.} - {2\;\pi\text{/}3}} \right\rbrack} \\{\cos\left\lbrack {{\omega_{{comp}.}t} + \phi_{{comp}.} - {4\;{\pi/3}}} \right\rbrack}\end{bmatrix}}}} & (5)\end{matrix}$

Substituting (5) into (3), the currents in (5) can be expressed in spacevector form:{right arrow over (i)} _(s) =Ie ^(jφ) e ^(jω) ^(s) ^(t) +I _(comp.) e^(jφ) ^(comp) e ^(jω) ^(comp) ^(t)  (6){right arrow over (i)} _(s) ={right arrow over (I)}e ^(jω) ^(s) ^(t)+{right arrow over (I)} _(comp.) e ^(jω) ^(comp) ^(t) ={right arrow over(i)}+{right arrow over (i)} _(comp.)  (7)

The resultant space vector is the sum of the fundamental and additionalcomponent corresponding space vectors. The locus of the space vectorwill no longer be a perfect circle due to the combined effect of thepresent components. Apart from distortion in the space vector'samplitude, the result is that the rotational speed of the space vectorfluctuates so that it does not pass any specific point on the circle atequal intervals 2π/ω_(s), but does so with fluctuating delays. The spacevector angular fluctuation (SVAF) method explores these fluctuations bymeasuring them with respect to the balanced referent signal of amplitudeI and angular frequency ω_(s). The resultant space vector from (6)divided by referent space vector is given in (8):

$\begin{matrix}{{\frac{\overset{\rightarrow}{i_{s}}}{\overset{\rightarrow}{i_{sr}}} = {1 + {\overset{\rightarrow}{I^{\prime}}{\mathbb{e}}^{{- j}\;\omega^{\prime}\; t}}}},} & (8)\end{matrix}$with

${\overset{\rightarrow}{I^{\prime}} = \frac{{\overset{\rightarrow}{I}}_{{comp}.}}{\overset{\rightarrow}{I}}},$and ω′=ω_(s)−ω_(comp.). Finally, the angular fluctuation of the spacevector may be found using equation (9):

$\begin{matrix}{\theta = {{\arctan\left( \frac{\overset{\rightarrow}{i_{s}}}{\overset{\rightarrow}{i_{sr}}} \right)} = {{\arctan\left( {- \frac{I^{\prime}\sin\;\omega^{\prime}t}{1 + {I^{\prime}\cos\;\omega^{\prime}\; t}}} \right)}.}}} & (9)\end{matrix}$

The following polynomial expansion (10) computes the arctangent of avariable x, when (x<1):arctan(x)=0.318253x+0.003314x ²−130908x ³+0.0068542x ⁴−0.009159x ⁵arctan(x)=α·x+β·x ² −γ·x ³ +δ·x ⁴ −ε·x ⁵  (10)

Since I′<<1, this expansion can be applied to (9). If only the firstterm of the expansion, αx, is taken into consideration, together withbinomial expansion for the argument of the arctangent function in (9),the following expression (11) is obtained:

$\begin{matrix}{\theta = {- {\alpha\left\lbrack {{I^{\prime}\sin\;\omega^{\prime}t} - {\frac{I^{\prime 2}}{2}\sin\; 2\;\omega^{\prime}t} + {\frac{I^{\prime 3}}{2}\left\lbrack {{\sin\;\omega^{\prime}t} + {\frac{1}{2}\left( {{\sin\; 3\;\omega^{\prime}t} - {\sin\;\omega^{\prime}t}} \right)}} \right\rbrack} - \ldots} \right\rbrack}}} & (11)\end{matrix}$

Equation (11) indicates frequencies that can be expected in the spectrumof the SVAF signal. To obtain the fluctuations in a real system, theinstantaneous rotational position θ_(i?) of the current space vector,which advances with each sample instant, is measured in the space vectorplane. When the expected increment in rotational positionΘ_(i)=ω_(s)t_(i) for uniform rotation at angular frequency ω_(s) issubtracted, fluctuations δθ_(i) in the angular position are obtained andgiven by (12):δθ_(i)=θ_(i)−ω_(s) t _(i),  (12)

where θ_(i) is the sampled phase angle of the rotating space vector ofthe real system during faulty condition, ω_(s) is the supply angularfrequency and t_(i) is the sample time. A voltage signal can be taken asa referent signal, since it will hold accurate information about mainssupply frequency currently present in the signal. Alternatively, equallyspaced samples from the space vector circle can be taken and from twoconsecutive samples and the expected rotation increment Θ can besubtracted, as in equation (13):δθ_(i)=θ_(i)−θ_(i−1)−Θ.  (13)

Θ is the expected, undisturbed rotation of the space vector betweensuccessive samples of the space vector taken at intervals t_(s) when thespace vector is sampled, where Θ equals ω_(s)t_(s). FIG. 26 illustratesan exemplary plot 600 of three phase currents 602, 604, and 606 withtime domain data with unbalanced harmonics plotted as current 608 versustime 610 in the time domain. Instants of time at which samples for SVAFanalysis are taken, are indicated with crosses 612, wherein there are 24samples taken in the single cycle (e.g., at 50 Hz) illustrated.

FIG. 27 illustrates an exemplary plot 620 of Iq component 622 versus Idcomponent 624, wherein a circular space vector 626 is illustrated indashed line for the case of an ideal balanced system without harmonics,and where an exemplary space vector 628 is illustrated for the case ofthree phase system with unbalanced harmonics (solid). Samples chosen forthe SVAF calculations from the space vector circle are marked withcrosses 630. Referring also to FIG. 28, an exemplary plot 640 of spacevector angular fluctuations SVAF 642 versus time 644 illustrates sampledvalues δθ_(i) 646 presented in the time domain. With the SVAF techniquesaccording to the present invention, it is possible to select anysampling frequency to sample the space vector circle, whereas the samplerate of the zero crossing times ZCT technique is limited. Thus, thefrequency range in the spectrum of the SVAF signal may be extended,whereby aliasing from higher frequencies may be avoided.

As illustrated in FIG. 29, an exemplary plot 650 of SVAF 652 versusfrequency 654 shows an exemplary frequency spectrum 656 of the spacevector angular fluctuation. The inventors have found that one or morefault indicative frequencies are present in the SVAF spectrum 656. Inaddition, the spectral data includes lines at frequencies correspondingto the physical process. For example, torque fluctuations at shaftfrequency will appear at exactly rotor frequency (f_(r)), compared tospectra from direct current sampling which provide this information assidebands to the mains frequency, or its harmonics. Other frequencies ofinterest are illustrated and described in greater detail hereinafter.

Experimental data has been obtained using three induction motors, withspecial windings on the stator to enable stator fault experiments. A 4hp motor was used in which slot windings were brought out to panelterminals, to allow short circuiting of selected slots to simulate astator fault. The motor was connected as a four pole induction motorwith stator windings in 46 slots, each slot having 64 turns. The rotorof the 4 hp motor was a wound rotor, which enabled the rotor circuit tobe unbalanced by adding extra resistance to simulate rotor faults. Alsoused were a 2 hp, four pole induction motor, and a 2.2 kW, four poleinduction motor, in which stator faults were simulated through taps onthe winding, that could be connected to short circuit two, three, fouror ten neighboring turns. A resistor was added to the shorting link tolimit the fault current to protect the winding. In addition, rotorfaults were simulated by cutting rotor bars.

With respect to stator fault detection, short-circuited turns on thestator of an induction motor cause asymmetry of the three phase statorwinding. When explained by symmetrical components theory, the overalleffect of such a fault is the presence of three phase negative sequencecurrents, as is known. Negative sequence currents rotate at angularfrequency (−ω_(s)) and so does their corresponding space vector. Thus,the three phase system from equation (5) has negative sequence currentsas additional component and angular fluctuations of the resultant spacevector from equation (11) include terms with following frequencyω′=ω_(s)−(−ω_(s))=2ω_(s). The spectrum of the SVAF signal will include aspectral component at 2f_(s), where f_(s) is the frequency of powerapplied to the motor. The amplitude of this spectral component has beenfound by the inventors to change when a stator fault condition exists.Consequently, the amplitude of the SVAF at this frequency may beemployed as a diagnostic measure for such stator faults.

To monitor stator faults in real time, the changes of the 2f_(s)amplitude may be monitored. The Goertzel algorithm may be employed toextract this spectral component amplitude information using data sampledover a single supply cycle. The Goertzel algorithm advantageouslyprovides expression of the computation of the DFT as a linear filteringoperation. This may be used, for example, when the DFT is to be computedfrom a sequence of N samples, at a relatively small number M of values,where M≦log₂ N. In the present example, M=1 since only one DFT value isneeded from the block of input data. The length of the input data is N=6for the ZCT method, or more for SVAF method, depending on the selectedsampling rate. The Goertzel algorithm may thus be more efficient thanthe FFT algorithm for this type of calculation. Where the spectralinformation is thus obtained, e.g., through monitoring the behavior ofone spectral frequency or component over a period of one mains cycle, avery fast response time results, making it suitable for real time,on-line detection of stator faults.

An induction motor with 652 turns in each stator phase was simulated.One, two three, four and five turns respectively were short-circuited ina computer model and the results are illustrated in FIG. 30 as anexemplary plot 660 of 2f_(s) component amplitude 662 versus time 664.The 2f_(s) diagnostic metric 666 was extracted on-line and its amplitudeplotted, wherein the SVAF technique responds virtually immediately tovarious stator faults 670, 672, 674, 676, and 678, corresponding to 1,2, 3, 4, and 5 of 652 turns shorted, respectively, in the motor. TheSVAF methodology detects the first supply cycle having increasednegative sequence current, which is reflected in the diagnostic index666 (e.g., SVAF spectral component amplitude fluctuation), asillustrated in FIG. 30. The number of shorted turns, e.g., the severityof the fault, is also indicated with rise in the amplitude of this index666, wherein the fault was alternatively switched on and offrepetitively in FIGS. 30 and 32.

FIG. 31 illustrates an exemplary plot 680 of SVAF 682 versus frequency684, centered around the 2f_(s) frequency of 100 HZ, wherein a spectrum686 is illustrated in dashed line for a healthy motor, and an SVAFspectrum 688 is illustrated for the motor with a stator short circuit(solid), for 1.4% of phase voltage short-circuited turns, as obtainedexperimentally. As illustrated, the amplitude 690 of the component atapproximately 2fs for the faulted motor is higher than the correspondingamplitude 692 for the healthy motor. Further experimental results areillustrated in FIG. 32 as an exemplary plot 700 of 2f_(s) componentamplitude 702 versus time 704, wherein the 2f_(s) diagnostic metric 706was extracted on-line and its amplitude plotted. The SVAF techniqueresponds quickly to various stator faults 708, 710, and 712,corresponding to 0.7%, 1.05%, and 1.4% of stator turns shorted,respectively, in the motor.

The SVAF technique may further be employed to detect problems associatedwith unbalanced supply voltages, and to distinguish such faults fromstator faults in accordance with another aspect of the invention. Whenthe supply voltage is unbalanced and contains negative sequencevoltages, negative sequence currents result on the stator of theinduction motor. The 2f_(s) current component amplitude is directlyaffected when the supply voltage is unbalanced. The invention furtherprovides for distinguishing stator faults from supply unbalanceconditions based on angular fluctuations in current and voltage spacevectors.

For instance, analysis of diagnostic index 2f_(s) from voltage spacevector angular fluctuations may be combined with analysis of diagnosticindex 2f_(s) from the current space vector angular fluctuations, toclassify the condition affecting the motor. In this regard, theinventors have found that a sudden change in the current 2f_(s) spectralcomponent amplitude occurring within one supply cycle with acorresponding sudden change in the voltage 2f_(s) spectral componentamplitude indicates an unbalanced condition, whereas a sudden change inthe current 2f_(s) spectral component amplitude occurring within onesupply cycle without such a corresponding sudden change in the voltage2f_(s) spectral component amplitude indicates a stator fault. Thus,fuzzy logic systems or other techniques may be employed in thediagnostic component in order to distinguish unbalanced power and statorfault conditions in accordance with the present invention.

The invention further provides for detection and/or diagnosis of rotorproblems or faults within an electric motor using space vector angularfluctuation. The existence of a rotor cage fault has been found to causean electrical asymmetry of the rotor circuit of a motor. This asymmetrygives rise to a (1−2s)f_(s) spectral component in the stator current,wherein the amplitude of these sidebands reflects the extent of therotor asymmetry. For instance, the interaction of the (1−2s)f_(s)harmonic of the motor current with the fundamental air-gap flux producesspeed ripple at 2sf_(s) and gives rise to additional motor currentharmonics at frequencies given by the following equation (14):f _(rb)=(1+2ks)f _(s) , k=1, 2, 3 . . .  (14)

The three phase system of currents from (5), for rotor fault, includesadditional components at angular frequency ω_(comp.)=(1−2s)ω_(s) and thespace vector angular fluctuation from equation (11) holds sinusoidalcomponents at ω′=ω_(s)−ω_(comp.)=2sω_(s) and its multiples. Spectralanalysis of the experimental SVAF signals with rotor asymmetry confirmsthat this component appears in the spectrum of the SVAF signal and maybe used as a diagnostic index for rotor faults. In addition, spectralcomponents at sidebands (1−s)2f_(s) and (1+s)2f_(s), appear around2f_(s) in the SVAF spectrum and their amplitude increases with theseverity of the rotor asymmetry. The origin of these sidebands can befound when current space vector spectrum is analyzed. For instance, ifthe motor is supplied from a non-ideal power source, e.g., one which hasnegative sequence current on the stator due to the negative sequencevoltage, negative sidebands −(1−2s)f_(s) and −(1+2s) f_(s) are found inthe motor current spectrum. Thus, the SVAF spectrum includesω′=ω_(s)−ω_(comp.)=ω_(s)−[−(1+/−2s)ω_(s)]=(1+/−s)2ω_(s) components.Modulation of the 2f_(s) component in the SVAF spectrum by thesesidebands causes 2sf_(s) ripple in the 2f_(s) component, giving anindication of the rotor unbalance, when this frequency component ismonitored on-line.

Referring now to FIGS. 33-44, an exemplary plot 720 is illustrated inFIG. 33 as SVAF power 722 versus frequency 724, wherein a spectrum 726for a mildly unbalanced rotor is shown in dashed line, and a spectrum728 is shown for a rotor having greater imbalance, obtained bysimulation. For the simulation, different amounts of rotor unbalancewere created with respect to a healthy rotor resistance of R_(r)′=0.816Ohm. The first case of unbalance 726 assumes added resistance ofΔR_(r)′=0.1 Ohm in one rotor phase, while in the second case 728, anadded resistance of ΔR_(r)′=0.2 Ohm is used. Twice the slip frequency is6 Hz, and sidebands to the 2f_(s) component occur correspondingly at 94Hz and 106 Hz for a source frequency of 50 Hz. FIG. 34 illustrates anexemplary plot 730 of SVAF power 732 versus frequency 734, wherein arise in the 2sf_(s) and 4sf_(s) spectral components is seen due to onebroken bar. A normal spectrum 736 is illustrated in FIG. 34 in dashedline together with a faulted motor spectrum 738. As illustrated in FIG.34, the peak 740 for the normal motor is lower than the peak 742 for thefaulted motor case.

FIG. 35 illustrates an exemplary plot 750 of SVAF power 752 versusfrequency 754, wherein a rise in the (1−s)2f_(s) and (1+s)2f_(s)sidebands is illustrated from the mild rotor resistance imbalancespectrum 756 to a spectrum 758 for higher degree of imbalance, for theimbalance conditions discussed above with respect to FIG. 33, whereinthe good motor is illustrated in dashed lines and the bad motorperformance is illustrated in solid lines. In FIG. 36, a plot 760 ofSVAF power 762 versus time 764 illustrates oscillations of the 2f_(s)component at 2sf_(s) frequency, via mild imbalance curve 766 and higherimbalance curve 768 for the same degrees of rotor resistance unbalance.FIG. 37 provides a plot 770 of current space vector power 772 versusfrequency 774 illustrating an exemplary current space vector spectrum776, and FIG. 38 provides a plot 780 of voltage space vector power 782versus frequency 784 illustrating a voltage space vector spectrum 786.

FIGS. 37 and 38 illustrate the spectrum of the current and voltage spacevectors, respectively, under unbalanced rotor (ΔR_(r)′=0.1 Ohm) andunbalanced voltage supply condition of 0.6%, wherein supply voltageunbalance condition is the ratio of negative to positive sequencevoltage. This voltage negative sequence causes 4.86% unbalance in thestator currents. A spectral component at (−50 Hz) is present in thespectrum of the voltage space vector, as well as in the spectrum of thecurrent space vector. Negative sidebands −(1−2s)f_(s) and −(1+2s)f_(s)in the current spectrum are also distinguishable. These cause sidebands(1−s)2f_(s) and (1+s)2f_(s) in the SVAF spectrum, which are shown on theFIG. 39, which provides a plot 800 of SVAF power 802 versus frequency804. As illustrated in the spectrum 806 of FIG. 39, spectral components810, 812, 814, 816, 818, 820, 822, 824, 826, 828, and 830 are seen for2sf_(s), 4sf_(s), 6sf_(s), (1−s)6f_(s), (1−s)4f_(s), (1−s)2f_(s),2f_(s), (1+s)2f_(s), (1+s)4f_(s), and (1+s)6f_(s), respectively.

FIG. 40 illustrates a plot 840 of SVAF power 842 versus frequency 844for a normal motor spectrum 846 and a broken bar fault spectrum 848,wherein fault indicative frequency spectrum components 850 areillustrated. Results presented are for normal condition on the motor andfor one broken rotor bar, out of twenty six. Supply voltage is 1%unbalanced. FIGS. 34 and 40 show 2sf_(s), and (1−s)2f_(s) and(1+s)2f_(s) spectral components, respectively. FIG. 41 illustrates aplot 860 of 2f_(s) component amplitude 862 versus time 864 for a normalmotor 866 and for a motor having one broken bar 868. Finally, complexspectra of the current and voltage space vectors are presented on FIGS.42 and 43, respectively. FIG. 42 includes a plot 870 of current spacevector power 872 versus frequency 874 showing a spectrum 876, and FIG.43 illustrates a plot 880 of voltage space vector 882 versus frequency884 with a spectrum 886. Negative sequence current and voltage, as wellas −(1−2s)f_(s) negative current sideband can be clearly be seen. FIG.44 presents full spectrum with rotor fault indicative frequencies,wherein a plot 890 of SVAF power 892 versus frequency 894 illustrates aspectrum 896.

The angular fluctuations of the induction motor current space vector maythus be employed in accordance with the invention as diagnostic data forstator and rotor induction motor faults, as well as for diagnosingunbalanced supply conditions. The SVAF methodology allows monitoring offault indicative changes in the diagnostic data, through analyzingcharacteristic frequencies in the spectrum of the space vector angularfluctuations. The following table summarizes various diagnostic indicesfor stator and rotor faults which may be detected according to theinvention:

TABLE I DIAGNOSTIC INDICES FOR SVAF METHOD Diagnostic Origin of Requiredindex index for Monitoring type Trend Current Current Stator Real timeIncrease with neg. SVAF 2f_(s) neg. fault sequence sequence VoltageVoltage Stator Real time Increase with neg. SVAF 2f_(s) neg. faultsequence sequence Current (1 − 2s)f_(s) Rotor On-line Increase with SVAF2sf_(s) current fault spectrum fault sideband Current −(1 − 2s)f_(s)Rotor On-line Increase with SVAF current fault spectrum fault (1 −s)2f_(s) sideband Current −(1 + 2s)f_(s) Rotor On-line Increase withSVAF current fault spectrum fault (1 + s)2f_(s) sideband

Stator faults may thus be indicated in the spectrum as change of the2f_(s) and other component amplitudes. The Goertzel algorithm may beadvantageously employed to extract the component's amplitude from thesignal during each cycle of the mains supply. This avoids lengthy theprocedure required for creating the frequency spectrum by FourierTransformation as well as allowing real-time monitoring of motor faults.The 2f_(s) component will respond to the change in the angularfluctuations as soon as a fault happens, allowing fast recognition inaccordance with the present invention. The recognition process mayinclude detecting angular fluctuations of the voltage space vector,since voltage unbalance and load changes will affect this frequency.Rotor faults are indicated in the current space vector angularfluctuations spectrum with spectral lines at 2sf_(s) caused by(1−2s)f_(s) and (1+2s)f_(s) in the motor current spectrum, and at(1−s)2f_(s) and (1+s)2f_(s), which are directly caused by spectral lines−(1−2s)f_(s) and −(1+2s)f_(s) in the motor current space vectorspectrum. Additionally, the 2f_(s) SVAF spectral component willoscillate at 2sf_(s) when monitored during each cycle, giving indicationof the rotor unbalance in real-time.

Although the invention has been shown and described with respect tocertain illustrated aspects, it will be appreciated that equivalentalterations and modifications will occur to others skilled in the artupon the reading and understanding of this specification and the annexeddrawings. In particular regard to the various functions performed by theabove described components (assemblies, devices, circuits, systems,etc.), the terms (including a reference to a “means”) used to describesuch components are intended to correspond, unless otherwise indicated,to any component which performs the specified function of the describedcomponent (e.g., that is functionally equivalent), even though notstructurally equivalent to the disclosed structure, which performs thefunction in the herein illustrated exemplary aspects of the invention.In this regard, it will also be recognized that the invention includes asystem as well as a computer-readable medium having computer-executableinstructions for performing the acts and/or events of the variousmethods of the invention.

In addition, while a particular feature of the invention may have beendisclosed with respect to only one of several implementations, suchfeature may be combined with one or more other features of the otherimplementations as may be desired and advantageous for any given orparticular application. As used in this application, the term“component” is intended to refer to a computer-related entity, eitherhardware, a combination of hardware and software, software, or softwarein execution. For example, a component may be, but is not limited to, aprocess running on a processor, a processor, an object, an executable, athread of execution, a program, and a computer. Furthermore, to theextent that the terms “includes”, “including”, “has”, “having”, andvariants thereof are used in either the detailed description or theclaims, these terms are intended to be inclusive in a manner similar tothe term “comprising.”

1. A method for controlling a motorized system comprising: measuring anattribute of the motorized system, the attribute comprises at least oneof vibration, speed, temperature, pressure, and current in the motorizedsystem; diagnosing a health of the motorized system based on themeasured attribute; providing a diagnostics signal based on thediagnosed health; prognosing a state of the motorized system based atleast in part on the at least one sensed attribute and/or the diagnosedstate; providing a control signal based at least in part on thediagnosed health and the prognosed state; and providing a feedbackoperation that adjusts the control signal to extend the lifetime of themotorized system to a specific time horizon, the control signal isadjusted based upon at least one of reaching a pre-determined pumpcavitation amount or reaching a pre-determined pump blockage amount. 2.The method of claim 1, further comprising operating the motorized systemaccording to the diagnostics signal.
 3. The method of claim 1, furthercomprising modifying a setpoint of the motorized system.
 4. The methodof claim 1, wherein diagnosing the health comprises obtaining afrequency spectrum of the measured attribute and analyzing the frequencyspectrum to detect adverse operating conditions.
 5. The method of claim4, wherein analyzing the frequency spectrum comprises analyzing thefrequency spectrum to detect faults, component wear and componentdegradation.
 6. The method of claim 5, wherein measuring the attributecomprises measuring an attribute associated with a motorized pump. 7.The method of claim 1, wherein measuring the attribute comprisesmeasuring an attribute associated with a fan.
 8. The method of claim 1,wherein measuring the attribute comprises measuring an attributeassociated with the motorized system selected from the group comprisingmotorized pump, fan, conveyor system, compressor, gear box, motioncontrol device, screw pump, mixer, hydraulic machine and pneumaticmachine.
 9. The method of claim 1, wherein diagnosing the healthcomprises analyzing an amplitude of a first spectral component of afrequency spectrum at a first frequency.
 10. The method of claim 1,wherein providing the control signal comprises providing the controlsignal to increase cavitation to reduce damage to the motorized system.11. The method of claim 1, wherein providing the control signalcomprises providing the control signal to reduce cavitation to extend anoperating lifetime of the motorized system.
 12. The method of claim 1,wherein providing the control signal comprises generating the controlsignal and transmitting the control signal via a wireless network. 13.The method of claim 1, wherein providing the diagnostic signal comprisesgenerating the diagnostic signal and transmitting the diagnostic signalvia a wireless network.
 14. The method of claim 1 being implemented on asystem connected to the motorized system via a wireless network.
 15. Themethod of claim 1, wherein measuring the attribute comprises receivingmeasurements from at least one sensor.
 16. A control system forcontrolling a motorized system comprising: means for measuring anattribute of the motorized system, the measured attribute comprises atleast one of vibration, speed, temperature, pressure, and current in themotorized system; means for diagnosing a health of the motorized system;means for prognosing a state of the motorized system; means forproviding a control signal based at least in part on both of a diagnosedhealth and a prognosed state of the motorized system; means forproviding a diagnostic signal; and means for performing feedbackanalysis to adjust the control signal to extend motorized systemlifetime to a specific time horizon, the control signal is adjustedbased upon at least one of reaching a pre-determined pump cavitationamount or reaching a pre-determined pump blockage amount.
 17. Thecontrol system of claim 16, further comprising: means for modifyingoperation of the motorized system based on the diagnostic signal. 18.The control system of claim 16, further comprising: means for modifyingoperation of the motorized system based on the control signal.
 19. Asystem comprising: a motorized system; a communications link coupled tothe motorized system; and a control system coupled to the communicationslink comprising: a controller coupled to the communications link adaptedto operate the motorized system in a controlled fashion; a diagnosticssystem coupled to the communications link adapted to diagnose the healthof the motorized system according to at least one measured attributeassociated with the motorized system, the measured attribute comprisesat least one of vibration, speed, temperature, pressure, and current inthe motorized system; a prognostics system coupled to the communicationslink that provides prognoses of future states of the motorized systembased at least in part on the at least one sensed attribute and/or andthe diagnosed health and provides the prognoses to the controlcomponent; and a feedback analysis component that adjusts the controllervia a control signal to increase motorized system life duration to aspecific time horizon, the control signal is adjusted based upon atleast one of reaching a pre-determined pump cavitation amount orreaching a pre-determined pump blockage amount.
 20. The system of claim19, wherein the motorized system comprises components, devices,subsystems and process controls.
 21. The system of claim 20, wherein thecomponents comprise bearings, the devices comprise a motor, pump andfan, the subsystems comprise a motor-drive-pump and process controlscomprise a pump fluid control.
 22. The system of claim 19, wherein themotorized system comprises a motor and a load, and wherein the loadcomprises at least one of a valve, a pump, a conveyor roller, a fan, acompressor, and a gearbox.
 23. The system of claim 20, wherein thediagnostics system provides a diagnostics signal, and wherein thecontroller provides a control signal.
 24. The system of claim 23,wherein the diagnostics signal represents health of the motorized systemand the control signal represents control information for the motorizedsystem.
 25. The system of claim 20, wherein the controller provides acontrol signal, wherein the control signal contains control informationfor controlling at least one of the components, the devices, thesubsystems and the process controls.
 26. The system of claim 19, furthercomprising at least one sensor coupled to the motorized system and thecommunications link for measuring the at least one measured attribute.27. The system of claim 19, wherein the communications link is a wiredconnection.
 28. The system of claim 19, wherein the communications linkis a wireless connection.
 29. The system of claim 19, wherein thecommunications link is a wireless radio frequency system.
 30. The systemof claim 19, wherein the communications link is a wireless network. 31.The system of claim 19, wherein the control system is implemented on acomputer system.
 32. A system to facilitate controlling a motorizedsystem, comprising: at least one sensor that senses at least oneattribute of the motorized system, the attribute comprises at least oneof vibration, speed, temperature, pressure, and current in the motorizedsystem; a diagnostics system that diagnoses a state of the motorizedsystem based at least in part on the at least one sensed attribute; aprognostic system that makes a prognosis of the motorized system basedat least in part on the at least one sensed attribute, the diagnosedstate, or both; and a controller that controls the motorized system viaa control signal based at least in part on the diagnosed state; thediagnostics system further performs at least a second diagnosis of thestate of the motorized system after corrective action is taken by thecontrol component and ensures that the motorized system will functionuntil a predetermined time horizon is reached, the control signal isadjusted based upon at least one of reaching a pre-determined pumpcavitation amount or reaching a pre-determined pump blockage amount. 33.The system of claim 32, the controller controlling the motorized systembased at least in part on the prognosis.
 34. The system of claim 33, thecontroller automatically adjusting operation of the motorized systembased at least in part on prognosed future states of the motorizedsystem.
 35. The system of claim 32, the prognostic system comprising anon-linear training system.
 36. The system of claim 32, the prognosticsystem inferring future operating states of the motorized system. 37.The system of claim 32, the controller automatically adjusting anoperating state of the motorized system based at least in part on theprognosis.
 38. The system of claim 32, the controller schedulingpreventive maintenance for the motorized system based at least in parton the prognosis.